CHINESE

CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations (D19-58)

Challenge:	Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French.
Approach:	They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning .
Outcome:	The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French.

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Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan

Different types of syntactic agreement recruit the same units within large language models (2026.acl-long)

Challenge:	Recent large corpora of triplets have opened the door to supervised machine learning approaches for Question-Answering.
Approach:	They propose to generate questions from the semantic Frame analysis of large corpora using a CALOR-QUEST resource in French and use it to improve machine reading comprehension.
Outcome:	The proposed method generates questions from the semantic Frame analysis of large corpora and then tests them on the CALOR-QUEST resource in French.

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Daria Kryvosheieva, Andrea Gregor de Varda, Evelina Fedorenko, Greta Tuckute

What Can String Probability Tell Us About Grammaticality? (2026.tacl-1)

Challenge:	Large language models can reliably distinguish grammatical from ungrammatically sentences, but how gramatical knowledge is represented within the models remains an open question.
Approach:	They use a functional localization approach inspired by cognitive neuroscience to identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models.
Outcome:	The proposed model is most responsive to 67 English syntactic phenomena and consistently supports model performance.

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Jennifer Hu, Ethan Gotlieb Wilcox, Siyuan Song, Kyle Mahowald, Roger P. Levy

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	linguistic theories have argued that language models have largely achieved grammatical competence, but they will assign non-zero probability to all strings.
Approach:	They propose a theoretical framework for analyzing string probabilities in linguistics based on simple assumptions about the generative process of corpus data.
Outcome:	The proposed framework makes three predictions using 280K sentence pairs in English and Chinese.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

JSEEGraph: Joint Structured Event Extraction as Graph Parsing (2023.starsem-1)

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

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Huiling You, Lilja Vrelid, Samia Touileb

IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)

Challenge:	Existing approaches model event extraction using simplified datasets or sequence-labeling-based encodings.
Approach:	They propose a graph-based event extraction framework that explicitly encodes entities and events in a single semantic graph.
Outcome:	The proposed framework can handle nested event structures and solve different IE tasks jointly.

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)

Challenge:	Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities .
Approach:	They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens.
Outcome:	The proposed model improves the performance of LLMs for IE with zero-shot generalization.

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Bernd Bohnet, Chris Alberti, Michael Collins

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025.naacl-industry)

Challenge:	Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference.
Approach:	They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly.
Outcome:	The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese.

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Dezhi Ye, Haomei Jia, Junwei Hu, Tian Bowen, Jie Liu, Haijin Liang, Jin Ma, Wenmin Wang

MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset (2025.findings-acl)

Challenge:	Chinese Search Query Spell Correction is a task designed to identify and correct typographical errors within queries.
Approach:	They propose a large-scale benchmark specifically developed for Chinese Query Spell Correction.
Outcome:	The proposed benchmark covers a broad range of topics, including formal entities, everyday colloquialisms and idiomatic expressions.

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Jie Zhu, Junhui Li, Yalong Wen, Xiandong Li, Lifan Guo, Feng Chen

Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels (2026.eacl-srw)

Challenge:	Existing financial benchmarks rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings.
Approach:	They propose a multilingual, multi-sector, and multi-task dataset called MFinMeeting that supports English, Chinese, and Japanese .
Outcome:	The proposed benchmark supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts.

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Hikari Tanaka, Atsushi Keyaki, Mamoru Komachi

VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding (2024.findings-naacl)

Challenge:	Large language models (LLMs) can generate fluent text, but the quality of generated content depends on its consistency with the given input.
Approach:	They constructed a Japanese evaluation dataset for hallucination detection in summarization by manually annotating sentence-level faithfulness labels in LLM-generated summaries of Japanese documents.
Outcome:	The proposed model can detect hallucinations in Japanese documents by annotating faithfulness labels in Japanese summaries.

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Phong Do, Son Tran, Phu Hoang, Kiet Nguyen, Ngan Nguyen

Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation (2022.naacl-srw)

Challenge:	a lack of standard evaluation metrics and benchmarks makes it difficult to identify strengths of Vietnamese NLP models.
Approach:	They propose to establish a standardized set of benchmarks for Vietnamese NLU . they propose to evaluate Vietnamese language understanding models using a pre-trained model .
Outcome:	The proposed model combines proficiency of a multilingual pre-trained model with Vietnamese linguistic knowledge.

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Xiaomeng Pan, Hongfei Wang, Teruaki Oka, Mamoru Komachi

Humor Recognition Using Deep Learning (N18-2)

Challenge:	Word Sense Disambiguation (WSD) is a core task in natural language processing . ancient Chinese has rarely been used in WSD tasks due to lack of a dataset .
Approach:	They annotate ancient Chinese text Zuo Zhuan using a copyright-free dictionary . they apply a method to find the most appropriate sense in a context using k-NN .
Outcome:	The proposed dataset will be available on GitHub.

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Peng-Yu Chen, Von-Wun Soo

LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators (2025.emnlp-demos)

Challenge:	Humor is an essential but most fascinating element in personal communication.
Approach:	They propose a convolutional neural network with extensive filter size and filter number to increase the depth of networks.
Outcome:	The proposed model outperforms existing models on accuracy, precision and recall . the proposed model can learn to distinguish between humorous and nonhumorous texts .

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Leanne Tan, Gabriel Chua, Ziyu Ge, Roy Ka-Wei Lee

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)

Challenge:	a new study shows that moderation systems that ignore localisation and low-resource variants risk degraded performance and exploitation in real-world deployments.
Approach:	They propose a lightweight, multilingual moderation classifier tailored to Singapore's context . it uses pre-trained OpenAI embeddings and a multi-head ordinal classifier .
Outcome:	The proposed classifier outperforms commercial and open-source models across 17 benchmarks.

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Daniel Zhang-li, Jing Zhang, Jifan Yu, Xiaokang Zhang, Peng Zhang, Jie Tang, Juanzi Li

Knowledge Representation

Challenge:	Existing studies have explored the use of entity linking (EL) in downstream tasks.
Approach:	They propose a modularized entity linking toolkit for easy task adaptation.
Outcome:	The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods.

MultiMUC: Multilingual Template Filling on MUC-4 (2024.eacl-long)

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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi

Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)

Challenge:	We present multilingual parallel template filling datasets for MUCs . systems were required to extract one template per incident, containing details about perpetrators, victims, weapons used .
Approach:	They introduce MultiMUC, the first multilingual parallel corpus for template filling . they obtain automatic translations from a strong multilingual machine translation system .
Outcome:	The proposed dataset includes translations of the classic MUC-4 template filling benchmark into Arabic, Chinese, Farsi, Korean, and Russian.

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Yiran Rex Ma, Shan Huang, Yuting Xu, Ziyu Zhou, Yuanxi Wei

A Multilingual Multiway Evaluation Data Set for Structured Document Translation of Asian Languages (2022.findings-aacl)

Challenge:	Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation.
Approach:	They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English.
Outcome:	The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects.

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Bianka Buschbeck, Raj Dabre, Miriam Exel, Matthias Huck, Patrick Huy, Raphael Rubino, Hideki Tanaka

Neural Unsupervised Parsing Beyond English (D19-61)

Challenge:	a lack of evaluation data sets for structured content limits progress in machine translation . a common use case of machine translation is the translation of structured or formatted documents .
Approach:	They propose a multilingual multiway evaluation data set for machine translation of structured documents of Asian languages Japanese, Korean and Chinese.
Outcome:	The proposed data set is well suited for multilingual evaluation and contains richer annotation tag sets than existing data sets.

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Katharina Kann, Anhad Mohananey, Samuel R. Bowman, Kyunghyun Cho

Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions (P18-2)

Challenge:	Unsupervised parsing is a task that can be learned without substantial prior knowledge.
Approach:	They train an unsupervised model for Arabic, Chinese, English, and German to learn syntactic structure from unlabeled text.
Outcome:	The PRPN architecture outperforms trivial baselines and acquires at least some parsing ability for all languages.

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Dongyu Zhang, Hongfei Lin, Liang Yang, Shaowu Zhang, Bo Xu

Logographic Information Aids Learning Better Representations for Natural Language Inference (2022.findings-aacl)

Challenge:	a corpus of 5,605 manually annotated sentences in Chinese is described . emotion is an abstract and vague conception, which is often described by metaphor .
Approach:	They propose to construct a corpus of metaphors annotated with emotion in Chinese . they use an annotation scheme to include linguistic metaphors, emotional categories and intensity .
Outcome:	The proposed corpus contains 5,605 manually annotated sentences in Chinese . the authors show that the corpus is large enough to analyze emotions .

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Zijian Jin, Duygu Ataman

QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)

Challenge:	Statistical language models often ignore information related to logographic features of written text . many languages, including Chinese and Vietnamese, use logographic writing systems .
Approach:	They propose to provide language models with logographic information to learn better semantic representations.
Outcome:	The proposed model improves semantic representations in six languages with logographic writing systems.

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Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong Sun

Challenge:	Existing methods to recommend quotes are evaluated on unpublished datasets .
Approach:	They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese.
Outcome:	The proposed model outperforms existing methods on all three parts of QuoteR.

STREAM-ZH: Simplified Topic Retrieval Exploration and Analysis Module for Chinese Language (2026.eacl-short)

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Hongyi Li, Jianjun Lian, Anton Frederik Thielmann, Andre Python

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

Challenge:	Simplified Topic Retrieval Exploration and Analysis Module for Chinese language is the first topic modeling package to fully support the Chinese language.
Approach:	They propose a topic modeling package that fully supports the Chinese language . they use preprocessed textual datasets to assess topic models .
Outcome:	The proposed framework outperforms existing topic models using English-translated textual input.

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

Multiple Character Embeddings for Chinese Word Segmentation (P19-2)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Jianing Zhou, Jingkang Wang, Gongshen Liu

Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)

Challenge:	Chinese word segmentation is regarded as character-based sequence labeling task in most current work but it neglects important fact: Chinese characters contain both semantic and phonetic meanings.
Approach:	They propose a shared bi-LSTM-CRF model which fuses linguistic features efficiently by sharing the LSTM network during the training procedure.
Outcome:	The proposed model achieves state-of-the-art in AS and CityU corpora without external lexical resources.

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Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss

Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)

Challenge:	Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources.
Approach:	They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages.
Outcome:	The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions.

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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward

Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)

Challenge:	Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English.
Approach:	They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens.
Outcome:	The proposed technique outperforms the best results for German, Italian, Spanish and Chinese.

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Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou

Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)

Challenge:	Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available.
Approach:	They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities .
Outcome:	The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text.

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Chen Zhao, Yatu Ji, Ren Qing-Dao-Er-Ji, Nier Wu, Lei Shi, Fu Liu, Yepai Jia

Zero Pronouns Identification based on Span prediction (2021.acl-srw)

Challenge:	Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora.
Approach:	They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages.
Outcome:	The proposed framework improves on four low-resource agglutinative language tasks.

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Sei Iwata, Taro Watanabe, Masaaki Nagata

Challenge:	Pro-drop languages allow omissions of essential phrases or arguments . the presence of zero-pronouns affects downstream tasks of NLP .
Approach:	They propose a query-based method to identify zero-pronoun arguments . they use Japanese and Chinese datasets to evaluate the method .
Outcome:	The proposed method surpasses the sequence labeling baseline on Japanese and Chinese datasets.

A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)

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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu

ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)

Challenge:	Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters.
Approach:	They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases.
Outcome:	The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters .

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Reem Alghamdi, Zhenwen Liang, Xiangliang Zhang

Numerical Reasoning

Challenge:	This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages .
Approach:	They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance .
Outcome:	The proposed model improves Arabic MWP solvers by 3% over the existing model.

One-shot Learning for Question-Answering in Gaokao History Challenge (C18-1)

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Zhuosheng Zhang, Hai Zhao

Paraphrastic Representations at Scale (2022.emnlp-demos)

Challenge:	Existing work on deep question-answering tasks from admission exams is challenging since it requires effective representation to capture complicated semantic relations between questions and answers.
Approach:	They propose a hybrid neural model for deep question-answering task from history examinations using a gated network and a machine labeler.
Outcome:	The proposed model obtains substantial performance gains over baseline models in terms of multiple evaluation metrics.

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John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-kirkpatrick

GOLEMcoref: A Multilingual Coreference Dataset of Fiction (2026.acl-short)

Challenge:	a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages.
Approach:	They propose a system that allows users to train their own paraphrastic sentence representations in a variety of languages.
Outcome:	The proposed models outperform previous models on monolingual and cross-lingual tasks and can be used on CPUs with little difference in inference speed.

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Andreas Van Cranenburgh, Xiaoyan Yang, null Alvanita, Cecilia Nicole Di Domenico, Maria Ferragud, Arianna Graciotti, Byungjun Kim, Seonyeong Park, Noa Visser Solissa, Xiaoyu Zhou, Federico Pianzola

Transition-Based Chinese AMR Parsing (N18-2)

Challenge:	Despite considerable progress, most research still focuses predominantly on English . fictional texts bring additional challenges not covered by standard benchmark datasets .
Approach:	They present a multilingual coreference dataset of 827k fanfiction tokens in 7 languages . they discuss their annotation scheme and language-specific challenges .
Outcome:	The proposed dataset includes full stories of diverse lengths, ranging from 500 to 17k words.

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Chuan Wang, Bin Li, Nianwen Xue

Chinese Idiom Paraphrasing (2023.tacl-1)

Challenge:	Abstract Meaning Representation (AMR) is a semantic representation where the meaning of a sentence is encoded as a rooted, directed and acyclic graph.
Approach:	They propose a transition-based AMR parsing framework for Chinese to be used in the next generation of AMR.
Outcome:	The proposed parser is based on the Chinese AMR bank.

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

SRS-Stories: Vocabulary-constrained multilingual story generation for language learning (2025.emnlp-industry)

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

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Wiktor Kamzela, Mateusz Lango, Ondrej Dusek

ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models (2023.emnlp-demo)

Challenge:	Existing methods for learning foreign languages are to use a spaced repetition system to learn new vocabulary.
Approach:	They use large language models to generate personalized stories using only the vocabulary they know.
Outcome:	The generated stories are more grammatical, coherent, and provide better examples of word usage than the standard beam search approach.

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Baoli Zhang, Haining Xie, Pengfan Du, Junhao Chen, Pengfei Cao, Yubo Chen, Shengping Liu, Kang Liu, Jun Zhao

Towards Zero-Shot Multimodal Machine Translation (2025.findings-naacl)

Challenge:	Various types of LLMs have recently been rapidly developing, such as Llama2 and ChatGLM2 .
Approach:	They propose a benchmark that comprehensively evaluates LLMs across 7 ability dimensions covering 51 tasks.
Outcome:	The proposed benchmarks are comprehensive and systematic, with a high level of accuracy and authority.

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Matthieu Futeral, Cordelia Schmid, Benoît Sagot, Rachel Bawden

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Current multimodal machine translation systems rely on fully supervised data, which is costly to collect and prevents extension of MMT to language pairs with no such data.
Approach:	They propose a method to bypass the need for fully supervised data to train MMT systems . they adapt a strong text-only machine translation model to a visually conditioned language model and a divergence test set to evaluate how well models use images to disambiguate translations.
Outcome:	The proposed method can generalize to languages with no fully supervised training data.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus (2024.lrec-main)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Ye Tao, Chaofeng Lu, Meng Liu, Kai Xu, Tianyu Liu, Yunlong Tian, Yongjie Du

GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing (2022.aacl-short)

Challenge:	Existing methods to generate high-quality speech with limited target speaker corpus require extensive training data.
Approach:	They propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of synthesized speech is not significantly degraded.
Outcome:	The proposed method significantly reduces training costs while maintaining the naturalness of synthesized speech.

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Siyao Peng, Yang Janet Liu, Amir Zeldes

Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts (2024.naacl-short)

Challenge:	GCDT is the largest hierarchical discourse treebank for Mandarin Chinese in the framework of Rhetorical Structure Theory (RST).
Approach:	They propose to use a Chinese hierarchical discourse treebank to parse Mandarin Chinese using relation inventory and a multilingual training program.
Outcome:	The proposed dataset includes state-of-the-art scores for Chinese RST parsing and RST Parsing on the English GUM dataset, using cross-lingual training in Chinese and English with multilingual embeddings.

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Michael Saxon, Yiran Luo, Sharon Levy, Chitta Baral, Yezhou Yang, William Yang Wang

Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)

Challenge:	With growth in the popularity of text-to-image models has come interest in assessing their multilingual capabilities, including multilingual accessibility.
Approach:	They propose to correct translation errors in a concept list translated to seven languages and compare the outputs of the benchmark to those conditioned on the old.
Outcome:	The proposed benchmark contains translation errors in Spanish, Japanese, and Chinese.

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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou

Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach (2025.coling-main)

Challenge:	Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes.
Approach:	They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset.
Outcome:	The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets.

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Junrui Wang, Mengyang Qiu, Yang Gu, Zihao Huang, Jungyeul Park

Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)

Challenge:	errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems.
Approach:	They propose a method to assess end-to-end grammatical error correction systems using alignment-based alignment methods that reproduce and improve results from existing evaluation tools.
Outcome:	The proposed method reproduces and improves results from existing evaluation tools, such as errant, even when applied to raw text input.

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Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, Benjamin Van Durme

Compositional Generalization in Multilingual Semantic Parsing over Wikidata (2022.tacl-1)

Challenge:	Existing datasets for entity coreference resolution are limited to English and other languages are rare.
Approach:	They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi.
Outcome:	The proposed dataset re-annotates for coreference on TV transcripts and then leverages existing subtitle translations to create a multilingual corpus.

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Ruixiang Cui, Rahul Aralikatte, Heather Lent, Daniel Hershcovich

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

Challenge:	Semantic parsers are mostly designed for and evaluated on English resources, such as CFQ.
Approach:	They propose a method for creating a multilingual, parallel question-query dataset . they analyze compositional generalization of parsers in Hebrew, Kannada, Chinese, and English .
Outcome:	The proposed method analyzes compositional generalization of parsers in Hebrew, Kannada, Chinese, and English.

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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training (2022.emnlp-industry)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He

NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages (2023.eacl-main)

Challenge:	Existing knowledge-enhanced pre-trained language models (KEPLMs) can capture internal knowledge, but can't understand external background knowledge.
Approach:	They propose to use Chinese knowledge-enhanced pre-trained language models to improve context-aware representations via learning from structured relations in knowledge bases.
Outcome:	Experiments show that Chinese knowledge-enhanced pre-trained language models outperform strong baselines over various benchmark NLP tasks and in different model sizes.

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Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau, Rico Sennrich, Sebastian Ruder

Employing Glyphic Information for Chinese Event Extraction with Vision-Language Model (2024.findings-emnlp)

Challenge:	In Indonesia, many languages are endangered and some are even extinct due to the unavailability of data resources and benchmarks.
Approach:	They propose a high-quality multilingual parallel corpus that covers 10 local languages from Indonesia.
Outcome:	The proposed resource includes sentiment and machine translation datasets, and bilingual lexicons.

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Xiaoyi Bao, Jinghang Gu, Zhongqing Wang, Minjie Qiang, Chu-Ren Huang

Multilingual Verbalisation of Knowledge Graphs (2025.findings-emnlp)

Challenge:	Recent studies on event extraction have incorporated a variety of features, including textual elements and annotations.
Approach:	They propose a glyphic multi-modal Chinese event extraction model with hieroglyphic images to capture morphological structure from the sequence.
Outcome:	The proposed model can extract events from a Chinese and KBP Eval datasets at low cost.

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Yifei Song, William Soto Martinez, Anna Nikiforovskaya, Evan Parker Kelly Chapple, Claire Gardent

ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)

Challenge:	Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources.
Approach:	They explore how to scale KG-to-Text generation to languages with varying resources . they construct multilingual training data and test data for each language .
Outcome:	The proposed approach performs best on all 9 languages, compared with other approaches on low vs high resource languages and on in- v. out-of-domain data.

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Tingting Liu, Chengyu Wang, Xiangru Zhu, Lei Li, Minghui Qiu, Jun Huang, Ming Gao, Yanghua Xiao

Improving Context Modeling in Neural Topic Segmentation (2020.aacl-main)

Challenge:	Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images.
Approach:	They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models.
Outcome:	The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts.

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Linzi Xing, Brad Hackinen, Giuseppe Carenini, Francesco Trebbi

Challenge:	Recent work favors highly effective neural supervised approaches for topic segmentation but current neural solutions are limited in how they model context.
Approach:	They propose to enhance a hierarchical attention biLSTM network-based topic segmenter to better model context by adding a coherence-related auxiliary task and restricted self-attention.
Outcome:	The proposed model outperforms SOTA approaches on three datasets and on four real-world benchmarks.

RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining (2022.acl-long)

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Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo Ji, Jiarui Fang, Jie Zhou

Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)

Challenge:	Large-scale pretrained language models have achieved SOTA results on NLP tasks but are vulnerable to adversarial attacks especially for logographic languages like Chinese.
Approach:	They propose a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc.
Outcome:	The proposed model outperforms baselines on 5 Chinese NLU tasks without sacrificing performance on clean testsets.

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Ying Chen, Wenjun Hou, Xiyao Cheng, Shoushan Li

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

Challenge:	Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection.
Approach:	They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks.
Outcome:	The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs.

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Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

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Sheng-Lun Wei, Yu-Ling Liao, Yen-Hua Chang, Hen-Hsen Huang, Hsin-Hsi Chen

Tonal Salience in Cognitive Decline: In-Context MCI Detection with Multimodal LLMs (2026.acl-srw)

Challenge:	Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities.
Approach:	They construct and release a speech-augmented benchmark based on Global MMLU Lite and a data set spanning English, Chinese, and Korean.
Outcome:	The proposed model is robust to demographic factors but sensitive to language and option order, suggesting that speech can amplify structural biases.

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Christopher Song, Abdullah P. Rashed Ahmed

Primum Non Nocere: Before working with Indigenous data, the ACL must confront ongoing colonialism (2022.acl-short)

Challenge:	a recent study shows that tonal languages like Chinese have a higher classification performance than non-tonal languages like English.
Approach:	a new study examines the differences between tonal and non-tonal language classifications . they hypothesize that the difference is rooted in language typology . early cognitive decline is notoriously difficult to detect .
Outcome:	The proposed method compared to TAUKADIAL audio shows that Chinese and English perform better on Chinese . the findings suggest that language typology should inform the design of audio-based cognitive screening tools .

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Lane Schwartz

SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)

Challenge:	aCL researchers must acknowledge that Indigenous languages are not merely low resource languages . authors propose that the ACL draft and adopt an ethical framework for NLP research involving Indigenous languages based on the legacy of colonialism .
Approach:	They propose that the ACL draft and adopt an ethical framework for NLP researchers . they propose to draw on best practices drawn from the Indigenous studies literature .
Outcome:	The proposed ethical framework is drawn from the Indigenous studies literature . it would be ethical for researchers to engage with Indigenous languages .

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Zhichao Shi, Shaoling Jing, Yi Cheng, Hao Zhang, Yuanzhuo Wang, Jie Zhang, Huawei Shen, Xueqi Cheng

Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events.
Approach:	They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context.
Outcome:	The proposed framework reduces decline rate while maintaining similar attack success rate.

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Nikita Moghe, Mark Steedman, Alexandra Birch

AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models (2024.findings-emnlp)

Challenge:	Existing methods to make multilingual systems expensive and tedious introduce pipeline of errors.
Approach:	They propose to use pre-trained multilingual models to enhance the transfer learning process by intermediate fine-tuning of pretrained multi-lingual models.
Outcome:	The proposed approach improves on the cross-lingual dialogue state tracking task with only 10% of the target language task data and zero-shot setup respectively.

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Yuting Wei, Yuanxing Xu, Xinru Wei, Yangsimin Yangsimin, Yangfu Zhu, Yuqing Li, Di Liu, Bin Wu

A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction (2026.findings-acl)

Challenge:	AC-EVAL is a benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese.
Approach:	They propose a benchmark to assess the advanced knowledge and reasoning capabilities of LLMs in ancient Chinese.
Outcome:	AC-EVAL aims to assess the comprehension of ancient Chinese texts . the benchmark covers 13 tasks covering historical facts, geography, social customs, art, philosophy, classical poetry and prose.

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Chengguang Gan, Sunbowen Lee, Qingyu Yin, Yunhao Liang, Xinyang He, Hanjun Wei, Younghun Lim, Shijian Wang, Hexiang Huang, QingHao Zhang, Shiwen Ni, Tatsunori Mori

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning (2021.acl-long)

Challenge:	Existing work on the Mutual Reinforcement Effect in information extraction has not been empirically validated . 76 percent of the 21 sub-datasets exhibit the Mutual Reforcement effect across languages .
Approach:	They propose a multilingual MRE mix dataset that integrates 21 sub-datasets covering English, Japanese, and Chinese.
Outcome:	The proposed framework reduces manual annotation effort while preserving structural requirements of MRE tasks.

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Forrest Davis, Marten van Schijndel

Semi-Supervised Semantic Role Labeling with Cross-View Training (D19-1)

Challenge:	Existing work has shown that non-linguistic biases in language models obscure linguistic knowledge.
Approach:	They hypothesize competing linguistic processes within a language could obscure linguistic knowledge.
Outcome:	The proposed model can learn linguistic constraints in a language and their relative ranking, the authors show . linguistic biases can obscure underlying linguistic knowledge, they show a single phenomenon in four languages.

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Rui Cai, Mirella Lapata

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)

Challenge:	Recent approaches rely on expensive annotations and are unavailable in low resource scenarios (e.g., rare languages or domains).
Approach:	They propose an end-to-end SRL model which leverages unlabeled data and propose to reduce the annotation effort involved via semi-supervised learning.
Outcome:	The proposed model outperforms the state-of-the-art in English and consistently improves performance in other languages, including Chinese, German, and Spanish.

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Zuyi Bao, Rui Huang, Chen Li, Kenny Zhu

The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (2022.lrec-1)

Challenge:	Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge.
Approach:	They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling.
Outcome:	The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese.

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Paul McNamee, Kevin Duh

Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek (2026.acl-srw)

Challenge:	a corpus of over 200,000 microblog translations supports translation of thirteen languages into English . large collections of parallel text, or bitext, are increasingly available in many languages .
Approach:	They propose a corpus of over 200,000 microblog posts that supports translation of thirteen languages into English.
Outcome:	The proposed corpus contains over 200,000 translations of microblog posts in 13 languages . fine-tuning showed significant improvements in translation quality .

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Annabella Sakunkoo, Jonathan Sakunkoo

MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset (2022.coling-1)

Challenge:	a recent study found that large language models are biased overwhelmingly Anglocentric . a stereotype perceptual map is a framework for analyzing how ethnic groups are positioned along evaluative dimensions.
Approach:	They use a stereotype perceptual map to examine how ethnic groups are positioned along evaluative dimensions.
Outcome:	The stereotype perceptual map analyzes model behavior across languages, scripts, evaluative domains and models.

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Wing Yan Li, Julie Weeds, David Weir

An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation (2024.lrec-main)

Challenge:	Existing datasets for cross-lingual information retrieval (CLIR) are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance.
Approach:	They propose a dataset to evaluate CLIR systems' disambiguation ability based on polysemous common nouns with multiple possible translations.
Outcome:	The proposed dataset shows that it has a higher requirement on the ability of CLIR systems to disambiguate query terms.

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Supryadi Supryadi, Leiyu Pan, Deyi Xiong

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Recent years have witnessed that massively multilingual neural machine translation (MMNMT) achieves a remarkable progress in both high- and low-resource language translation.
Approach:	They propose to use a robustness evaluation benchmark dataset to assess the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise.
Outcome:	The proposed dataset is publicly available at https://github.com/ID-ZH-MTRobustEval.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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Nickil Maveli, Shay Cohen

Cross-Lingual Abstract Meaning Representation Parsing (N18-1)

Challenge:	Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking.
Approach:	They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span.
Outcome:	The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese.

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Marco Damonte, Shay B. Cohen

Do We Need Language-Specific Fact-Checking Models? The Case of Chinese (2024.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages.
Approach:	They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese.
Outcome:	The proposed method overcomes structural differences between the target languages and requires no gold standard data.

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Caiqi Zhang, Zhijiang Guo, Andreas Vlachos

A BERT-based Dual Embedding Model for Chinese Idiom Prediction (2020.coling-main)

Challenge:	Existing fact-checking models in other languages lack grounding in real-world claims . current models are constrained to a single domain, like COVID-19 .
Approach:	They propose a Chinese document-level evidence retriever that can be translated into Chinese . they then construct an adversarial dataset that is more robust toward biases .
Outcome:	The proposed method outperforms translation-based methods and multilingual language models and is more robust toward biases.

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Minghuan Tan, Jing Jiang

CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use (2025.emnlp-main)

Challenge:	Chinese idioms are special fixed phrases whose meanings are often highly idiomatic and non-compositional.
Approach:	They propose to use a BERT-based dual embedding model to encode contextual words and learn dual embeds of the idioms.
Outcome:	The proposed model performs better than the existing state of the art on a Chinese idiom cloze dataset.

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Yicheng Fu, Zhemin Huang, Liuxin Yang, Yumeng Lu, Zhongdongming Dai

LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization (2021.findings-acl)

Challenge:	Existing benchmarks focus on narrow tasks such as multiple-choice cloze tests, isolated translation, or simple paraphrasing.
Approach:	They propose a benchmark to measure Chinese idioms' cultural and contextual nuances . they evaluate 2,937 human-verified examples covering 1,765 common idiomes .
Outcome:	The proposed benchmarks achieve 95% accuracy on Evaluative Connotation, but only 85% on Appropriateness and 40% top-1 accuracy in Open Cloze.

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Weidong Guo, Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li, Jianbo Tang

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)

Challenge:	Pre-trained language models are trained based on single-grained tokenization, making it hard to learn the precise meaning of coarse-grain words and phrases.
Approach:	They propose a language model pretraining method that incorporates multi-grained information of input text into pre-trained language models.
Outcome:	The proposed method improves performance on CLUE and SuperGLUE in Chinese and English with little extra inference cost.

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Junnan Zhu, Yu Zhou, Jiajun Zhang, Chengqing Zong

Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion (C18-1)

Challenge:	Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation.
Approach:	They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary.
Outcome:	The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks.

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Naoki Otani, Hirokazu Kiyomaru, Daisuke Kawahara, Sadao Kurohashi

Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)

Challenge:	Existing efforts to build commonsense knowledge bases are expensive and lack quantity and quality between languages.
Approach:	They propose to project English commonsense knowledge into Japanese and Chinese with high precision.
Outcome:	The proposed method achieves top-10 accuracy on the crowdsourced English–Japanese benchmark and 18,747 facts of accurate Japanese commonsense within a very short period.

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Wanqiu Long, Xinyi Cai, James Reid, Bonnie Webber, Deyi Xiong

Challenge:	Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese.
Approach:	They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties.
Outcome:	The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style.

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (2025.findings-emnlp)

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Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Xin Zhao

HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment (2025.naacl-long)

Challenge:	Existing systems with opaque architectures are limiting deep search capabilities for web-augmented large language models.
Approach:	They propose a transparent and modular multi-agent framework to democratize deep search for LLMs.
Outcome:	The proposed framework outperforms open-source systems in deep reasoning tasks.

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Thibaud Leteno, Irina Proskurina, Antoine Gourru, Julien Velcin, Charlotte Laclau, Guillaume Metzler, Christophe Gravier

Challenge:	HistoiresMorales is a dataset based on moralStories in French . it is based upon annotations of moral values within the dataset .
Approach:	They propose a dataset in French that aims to align language models with moral values . they use annotations to ensure their alignment with French norms .
Outcome:	The proposed dataset guarantees grammatical accuracy and adaptation to the French cultural context.

GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models (2022.emnlp-main)

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Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, Kai-Wei Chang

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	Recent work shows that Pre-trained Language Models store relational knowledge and utilize it for performing downstream tasks.
Approach:	They propose a benchmark dataset for probing the diversity of relational knowledge in multilingual PLMs.
Outcome:	The proposed dataset contains 3125 prompts in English, Chinese, Hindi, Persian, and Swahili . larger multilingual PLMs variants do not store geo-diverse concepts better than its smaller variant .

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Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)

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Jiali Chen, Xusen Hei, Yuqi Xue, Zihan Wu, Jiayuan Xie, Yi Cai

Speaking Multiple Languages Affects the Moral Bias of Language Models (2023.findings-acl)

Challenge:	Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA.
Approach:	They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning.
Outcome:	The proposed method significantly improves performance in automatic and human evaluation.

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Katharina Haemmerl, Bjoern Deiseroth, Patrick Schramowski, Jindřich Libovický, Constantin Rothkopf, Alexander Fraser, Kristian Kersting

PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation (2023.emnlp-main)

Challenge:	Pre-trained multilingual language models are often better on English than other languages . however, they are trained on varying amounts of data for each language .
Approach:	They apply the MORALDIRECTION framework to multilingual models and analyse their results . they find that PMLMs encode differing moral biases, but these do not correspond to cultural differences or commonalities in human opinions.
Outcome:	The proposed model captures moral norms from English and imposes them on other languages.

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Ke Wang, Xiutian Zhao, Yanghui Li, Wei Peng

MELA: Multilingual Evaluation of Linguistic Acceptability (2024.acl-long)

Challenge:	Pro-drop (‘pronoun-dropping’) language requires NMT systems to recover omitted pronouns, but this task lacks sufficient datasets for benchmarking .
Approach:	They propose a benchmarking method that leverages the semantic embedding of dropped pronouns to augment training pairs to alleviate the negative impact introduced by pro-drop .
Outcome:	The proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality on four Chinese-English translation corpora.

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Ziyin Zhang, Yikang Liu, Weifang Huang, Junyu Mao, Rui Wang, Hai Hu

A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)

Challenge:	Existing benchmarks on linguistic acceptability have been used to evaluate language models' ability to distinguish between acceptable and unacceptable sentences.
Approach:	They present the largest benchmark to date on linguistic acceptability: MELA . they establish LLM baselines on this benchmark and investigate cross-lingual transfer in acceptability judgements with XLM-R.
Outcome:	The proposed model outperforms open-source models on cross-lingual transfer in acceptability judgements.

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Yunlong Liang, Fandong Meng, Chulun Zhou, Jinan Xu, Yufeng Chen, Jinsong Su, Jie Zhou

Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese (2024.emnlp-main)

Challenge:	Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective.
Approach:	They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder .
Outcome:	The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting.

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Yuqi Chen, Sixuan Li, Ying Li, Mohammad Atari

Improving Constituency Parsing with Span Attention (2020.findings-emnlp)

Challenge:	Humans have produced written language for thousands of years, but most computational work is focused on contemporary languages and corpora.
Approach:	They propose a pipeline for historical-psychological text analysis in classical Chinese . they propose an indirect contrastive learning approach that fine-tunes pre-trained models .
Outcome:	The proposed pipeline outperforms word-embedding-based approaches across all tasks and exceeds prompting with GPT-4 in most tasks.

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Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang

Challenge:	Constituency parsing is a fundamental task for natural language understanding . n-grams are a conventional type of feature for contextual information . experimental results show that neural parsers with no grammar rules outperform statistical ones .
Approach:	They propose to incorporate n-grams into span representations by weighting them according to their contributions to the parsing process.
Outcome:	The proposed approach outperforms existing statistical grammar-based models on Arabic, Chinese, and English datasets.

A-TASC: Asian TED-Based Automatic Subtitling Corpus (2025.acl-long)

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Yuhan Zhou, Naoki Yoshinaga

Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)

Challenge:	Existing AS corpora and primary metric SubER focus on European languages.
Approach:	They propose an Asian TED-based automatic subtitling corpus derived from English TED Talks and a modification of SubER to enable reliable evaluation of subtitle quality for languages without explicit word boundaries.
Outcome:	The proposed corpus is based on TED Talks audio segments, transcripts, and subtitles in Chinese, Japanese, Korean, and Vietnamese.

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Xu Han, Yuqi Luo, Weize Chen, Zhiyuan Liu, Maosong Sun, Zhou Botong, Hao Fei, Suncong Zheng

Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)

Challenge:	Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages.
Approach:	They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
Outcome:	The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data.

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Chia-Wen Lu, Ching-Wen Yang, Wei-Yun Ma

Automatic Data Visualization Generation from Chinese Natural Language Questions (2024.lrec-main)

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity.
Approach:	They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples.
Outcome:	The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision.

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Yan Ge, Victor Junqiu Wei, Yuanfeng Song, Jason Chen Zhang, Raymond Chi-Wing Wong

Challenge:	Existing studies on data visualization generation from natural languages have not been conducted on Chinese Text-to-Vis.
Approach:	They propose to generate a Chinese text-to-vis dataset using a multilingual encoder and a cross-lingual ability.
Outcome:	The proposed dataset is challenging and deserves further research.

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)

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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam

Who Relies More on World Knowledge and Bias for Syntactic Ambiguity Resolution: Humans or LLMs? (2025.naacl-long)

Challenge:	X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research .
Approach:	They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source .
Outcome:	The proposed toolkit accelerates the post-editing of a new language dataset after translation.

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So Young Lee, Russell Scheinberg, Amber Shore, Ameeta Agrawal

A Compliance Checking Framework Based on Retrieval Augmented Generation (2025.coling-main)

Challenge:	Among various types of ambiguity, this study focuses on syntactic ambiguities, specifically relative 1 Dataset available at https://github.com/PortNLP/ MultiWHO.
Approach:	They propose to use a dataset to fine-grained evaluate relative clause attachment preferences in ambiguous and unambiguous contexts.
Outcome:	The proposed dataset shows that large language models perform well in unambiguous cases, but lack flexibility in human language processing.

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Jingyun Sun, Zhongze Luo, Yang Li

FinMTEB: Finance Massive Text Embedding Benchmark (2025.emnlp-main)

Challenge:	Existing text-based compliance checking methods are limited by their flexibility and lack structure.
Approach:	They propose a text-based compliance checking framework based on Retrieval-Augmented Generation that integrates a static layer for storing factual knowledge, a dynamic layer for retrieval and reasoning, and an eventic graph to structurally describe regulatory information.
Outcome:	The proposed framework consistently achieves state-of-the-art results across various scenarios surpassing baselines.

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Yixuan Tang, Yi Yang

Neural Multitask Learning for Simile Recognition (D18-1)

Challenge:	Existing text embedding benchmarks for financial domains are inadequately addressing the nuanced requirements of specialized domains like finance.
Approach:	They propose a finance-adapted embedding model that outperforms general-purpose models . they also introduce a new model, Fin-E5, which is also open-sourced .
Outcome:	The proposed framework outperforms general-purpose models on financial embedding tasks.

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Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, Guoping Hu

A Survey on Zero Pronoun Translation (2023.acl-long)

Challenge:	Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects.
Approach:	They propose a neural network framework for simile sentence classification, simile component extraction and language modeling.
Outcome:	The proposed framework outperforms rule-based and feature-based approaches in simile sentence classification and simile component extraction tasks.

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Longyue Wang, Siyou Liu, Mingzhou Xu, Linfeng Song, Shuming Shi, Zhaopeng Tu

Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains .
Outcome:	The proposed method and methods are compared to other models and evaluation metrics on different benchmarks.

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Yuanhe Tian, Yan Song, Fei Xia

Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)

Challenge:	Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective .
Approach:	They propose a character-based neural model enhanced by multi-channel attention of n-grams.
Outcome:	The proposed model outperforms baseline models on five benchmark datasets.

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Yang Gu, Zihao Huang, Min Zeng, Mengyang Qiu, Jungyeul Park

MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

Challenge:	In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners.
Approach:	They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings.
Outcome:	The proposed framework can provide detailed feedback on 12-16% of all errors, revealing subtle differences in error patterns between L1 and L2 writings.

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Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, Lei Li

Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs (2025.acl-long)

Challenge:	Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach:	They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome:	The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.

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Yanzhu Guo, Simone Conia, Zelin Zhou, Min Li, Saloni Potdar, Henry Xiao

SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant? (2025.emnlp-main)

Challenge:	Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated.
Approach:	They propose to use automatic corpus-level metrics to assess lexical and syntactic naturalness of LLMs in a multilingual context.
Outcome:	The proposed method improves naturalness of LLMs in target languages without compromising performance on general-purpose benchmarks.

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Xudong Lu, Haohao Gao, Renshou Wu, Shuai Ren, Xiaoxin Chen, Hongsheng Li, Fangyuan Li

Challenge:	Existing evaluation benchmarks for Large Language Models focus on objective tasks like mathematics and coding in English, which do not reflect the practical use cases of on-device LLMs in real-world mobile scenarios.
Approach:	They propose a benchmark to evaluate the capabilities of on-device Large Language Models in Chinese mobile contexts.
Outcome:	The proposed framework evaluates on-device LLMs and MLLMs in Chinese . it provides a standardized framework for evaluating LLM performance on real smartphones .

XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)

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Rexhina Blloshmi, Rocco Tripodi, Roberto Navigli

WYWEB: A NLP Evaluation Benchmark For Classical Chinese (2023.findings-acl)

Challenge:	Abstract Meaning Representation (AMR) is a popular formalism of natural language.
Approach:	They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages .
Outcome:	The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish.

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Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong, Yin Zhang

Challenge:	Existing benchmarks for classical Chinese are inadequate to evaluate performance of different NLP models.
Approach:	They propose an evaluation benchmark for classical Chinese NLP, which evaluates existing models.
Outcome:	The proposed benchmark evaluates the performance of existing models in classical Chinese.

Modal Dependency Parsing via Language Model Priming (2022.naacl-main)

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Jiarui Yao, Nianwen Xue, Bonan Min

Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)

Challenge:	modal dependency parsing is a task of parse a text into its modal dependence structure . the root node of an MDS is always the author of a document, the ultimate source of information sources .
Approach:	They propose a modal dependency parser based on priming pre-trained language models and evaluate it on two data sets.
Outcome:	The proposed parser improves on two data sets.

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Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang

More Than Words: Collocation Retokenization for Latent Dirichlet Allocation Models (2022.findings-acl)

Challenge:	Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles.
Approach:	They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data .
Outcome:	Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases .

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Jin Cheevaprawatdomrong, Alexandra Schofield, Attapol Rutherford

Challenge:	Latent Dirichlet Allocation models ingest words to discover their latent topics . but it is unclear how to achieve the best results for languages without marked word boundaries .
Approach:	They propose to use retokenization to merge frequent token ngrams into collocations in input to a Latent Dirichlet Allocation model.
Outcome:	The proposed model improves topic coherence and coherency in Chinese and Thai . the proposed model is more coherent and clearer than unmerged models .

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment (2023.emnlp-main)

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Sky CH-Wang, Arkadiy Saakyan, Oliver Li, Zhou Yu, Smaranda Muresan

Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)

Challenge:	Current research on developing computational models of social norms has focused on American society.
Approach:	They propose to leverage a Chinese Q&A platform and a socialchiemistry dataset as proxies for contrasting cultural axes and align social situations cross-culturally.
Outcome:	The proposed model can reason across cultures using a Chinese Q&A platform and the existing socialChemistry dataset.

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Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin

Cross-Lingual Leveled Reading Based on Language-Invariant Features (2021.findings-emnlp)

Challenge:	Existing methods of lexical sememe prediction rely on external context information of words to represent meaning.
Approach:	They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words.

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Simin Rao, Hua Zheng, Sujian Li

Compound Probabilistic Context-Free Grammars for Grammar Induction (P19-1)

Challenge:	Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers.
Approach:	They propose to use adversarial training and cross-lingual pre-training methods to transfer LR knowledge from annotated data in resource-rich English to Chinese.
Outcome:	The proposed method captures language-invariant features between English and Chinese.

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Yoon Kim, Chris Dyer, Alexander Rush

Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)

Challenge:	Existing approaches to grammar induction have resorted to manually-engineered features and auxiliary objectives to induce the desired structures.
Approach:	They propose a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar.
Outcome:	Experiments on English and Chinese show that the proposed approach is more efficient than other methods.

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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao

Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)

Challenge:	This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability.
Approach:	They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná .
Outcome:	The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed .

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Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou

A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)

Challenge:	Experimental results show that cross-language data expansion results in performance degradation.
Approach:	They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus.
Outcome:	The proposed method improves ED performance by 1.6% over the straight data combination.

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Kiet Nguyen, Vu Nguyen, Anh Nguyen, Ngan Nguyen

Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)

Challenge:	despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents.
Approach:	They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles .
Outcome:	The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia.

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Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Lam

CLiMP: A Benchmark for Chinese Language Model Evaluation (2021.eacl-main)

Challenge:	Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph.
Approach:	They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation .
Outcome:	The proposed model outperforms the current state-of-the-art English-only parser on four different languages.

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Beilei Xiang, Changbing Yang, Yu Li, Alex Warstadt, Katharina Kann

Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)

Challenge:	Linguistically informed analyses of language models (LMs) contribute to understanding and improvement of such models.
Approach:	They introduce a corpus of Chinese linguistic minimal pairs (CLiMP) to investigate what knowledge Chinese LMs acquire.
Outcome:	The proposed corpus of Chinese linguistic minimal pairs (CLiMP) covers 9 major Chinese linguist phenomena.

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Zhihao Zhang, Sophia Yat Mei Lee, Dong Zhang, Shoushan Li, Guodong Zhou

CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (2022.naacl-main)

Challenge:	Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences.
Approach:	They propose an entity-aligned translation approach to align entities between NSL and English .
Outcome:	The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages.

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Xuming Hu, Zhijiang Guo, GuanYu Wu, Aiwei Liu, Lijie Wen, Philip Yu

ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)

Challenge:	CHEF dataset provides evidence retrieval over non-English claims . e-fact-checking is a time-consuming task, which can take journalists several hours or days.
Approach:	They construct a dataset of 10K real-world claims that is based on annotated evidence retrieved from the Internet.
Outcome:	The proposed dataset provides evidence retrieval as a latent variable and can be used to train and reason over non-English claims.

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Jiaming Zhou, Yujie Guo, Shiwan Zhao, Yao Lu, Jianye Wang, Haoqin Sun, Hui Wang, Yong Qin

XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)

Challenge:	Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora.
Approach:	They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers.
Outcome:	The proposed model improves in-domain and cross-domain performance on children's speech.

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Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei

Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings (C18-1)

Challenge:	Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization.
Approach:	They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding.
Outcome:	The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks.

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Chenggang Mi, Yating Yang, Lei Wang, Xi Zhou, Tonghai Jiang

Computational Modeling of Affixoid Behavior in Chinese Morphology (2020.coling-main)

Challenge:	Almost every natural language processing task suffers from data sparseness.
Approach:	They propose a method which identify loanwords in monolingual corpora by using cross-lingual word embeddings as core feature and a log-linear model which combines several shallow features to predict the final results.
Outcome:	The proposed method outperforms baseline models significantly on loanword identification and translation in four languages and eight translation directions.

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Yu-Hsiang Tseng, Shu-Kai Hsieh, Pei-Yi Chen, Sara Court

Morphology

Challenge:	affixoid behavior in Mandarin Chinese is unclear due to polysemy and diachronic dynamics.
Approach:	They propose to use three quantitative features to model affixoid behavior in Mandarin Chinese to determine its status.
Outcome:	The proposed model shows that there are no clear criteria that can be used to identify an affix’s status in an isolating language like Mandarin Chinese.

A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder (2020.findings-emnlp)

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Xipeng Qiu, Hengzhi Pei, Hang Yan, Xuanjing Huang

An Empirical Investigation of Error Types in Vietnamese Parsing (C18-1)

Challenge:	Existing work on multicriteria Chinese word segmentation focuses on combining multiple heterogeneous segmentation criteria into a single task.
Approach:	They propose a unified model for multi-criteria Chinese word segmentation which is fully-shared for all criteria.
Outcome:	The proposed model outperforms existing models on eight datasets with different criteria.

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Quy Nguyen, Yusuke Miyao, Hiroshi Noji, Nhung Nguyen

VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)

Challenge:	Syntactic parsing improves the quality of natural language processing tasks.
Approach:	They evaluated Vietnamese Treebank model to find most suitable parsing method . they found that Vietnamese parsers produced limited training data and POS errors .
Outcome:	The proposed method improves the parsing quality in Vietnamese . the results highlight three possible sources of parser errors .

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Xiaoman Wang, Dan Yuan, Xin Liu, Yike Zhao, Xiaoxiao Zhang, Xizhi Chen, Yunshi Lan

Visual Data in NLP

Challenge:	Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios.
Approach:	They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments .
Outcome:	The proposed approach achieves an F 0.5 score of only 28.9%.

Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)

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Ao Wang, Xinghao Yang, Chen Li, Bao-di Liu, Weifeng Liu

A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)

Challenge:	Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied.
Approach:	They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions .
Outcome:	The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability .

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Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si

Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese (2022.naacl-main)

Challenge:	Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks .
Approach:	They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks.
Outcome:	The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese.

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Chenxuan Cui, Katherine J. Zhang, David Mortensen

An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

Challenge:	phonological hierarchies that predict coordinate constructions are often phonetically “natural” . a neural sequence labeling model can learn elaborate expressions in Hmong without using phonology information.
Approach:	They propose that coordinate compounds and elaborate expressions can be learned empirically by phonological hierarchies and a neural sequence labeling model can learn the ordering of elaborate expression in Hmong without using phonology.
Outcome:	The proposed models beat strong baselines for all three languages and learn hierarchies similar to those proposed by Mortensen.

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Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang

Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

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Chia-Chien Hung, Anne Lauscher, Ivan Vulić, Simone Ponzetto, Goran Glavaš

Summarising Historical Text in Modern Languages (2021.eacl-main)

Challenge:	Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas.
Approach:	They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models .
Outcome:	The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks .

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Xutan Peng, Yi Zheng, Chenghua Lin, Advaith Siddharthan

Incorporating Word Attention into Character-Based Word Segmentation (N19-1)

Challenge:	Historical text summarisation is a routine for historians and digital humanities researchers but has never been automated.
Approach:	They propose a model that can be trained even with no cross-lingual data and further benchmark it against state-of-the-art algorithms.
Outcome:	The proposed model outperforms standard cross-lingual benchmarks on historical text summarisation task and identifies distinctness and value of the dataset.

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Shohei Higashiyama, Masao Utiyama, Eiichiro Sumita, Masao Ideuchi, Yoshiaki Oida, Yohei Sakamoto, Isaac Okada

Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations (2024.acl-long)

Challenge:	Word segmentation models are used to minimize the effort in feature engineering.
Approach:	They propose a character-based model that learns the importance of multiple candidate words for a corresponding character on the basis of an attention mechanism and makes use of it for segmentation decisions.
Outcome:	The proposed model outperforms the state-of-the-art models on Japanese and Chinese benchmark datasets.

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Gregor Geigle, Radu Timofte, Goran Glavaš

Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning (N19-1)

Challenge:	Vision-and-language models with separate encoders for each modality are limited in availability.
Approach:	They propose a multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation.
Outcome:	The proposed model outperforms models on English and low-resource languages.

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Arseny Tolmachev, Daisuke Kawahara, Sadao Kurohashi

Personalized Text Retrieval for Learners of Chinese as a Foreign Language (C18-1)

Challenge:	Modern neural morphological analyzers consume gigabytes of memory.
Approach:	They propose a method which uses unigram character embeddings to train a model on labels produced by a state-of-the-art analyzer.
Outcome:	The proposed model outperforms dictionary-based methods in Japanese and Chinese . it uses less than 15 megabytes of space and is much smaller than the dictionary- based one .

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Chak Yan Yeung, John Lee

Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (D18-1)

Challenge:	a personalized text retrieval algorithm helps language learners select the most suitable reading material in terms of vocabulary complexity.
Approach:	They propose a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity.
Outcome:	The proposed algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficient learners.

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Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz

LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction (2026.eacl-long)

Challenge:	Recent attempts to improve grammar induction systems by bounding complexity of the model have not been compared against unbounded induction models.
Approach:	They propose to use a Bayesian induction inducer to limit the search space of the model and then sample trees with or without bounding.
Outcome:	The proposed model produces trees more accurately than or competitively with state-of-the-art constituency grammar induction models.

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Junior Cedric Tonga, Chen Cecilia Liu, Iryna Gurevych, Fajri Koto

Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)

Challenge:	Large language models encode rich cultural knowledge, but it remains mostly implicit and unstructured, limiting its interpretability and use.
Approach:	They propose an iterative framework for constructing a Cultural Commonsense Knowledge Graph using a prompt-based framework.
Outcome:	The proposed framework improves cultural reasoning and story generation on non-English cultures.

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Ye Wang, Ruijun Jiang, Zhongqing Wang, Guodong Zhou

DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation (2022.acl-long)

Challenge:	Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce.
Approach:	They propose a multilingual pre-trained language model that leverages bilingual pre-training to leverage aspects-based sentiment analysis.
Outcome:	The proposed model outperforms state-of-the-art models across multiple languages.

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Niccolò Campolungo, Federico Martelli, Francesco Saina, Roberto Navigli

Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)

Challenge:	Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation.
Approach:	They propose a new benchmark to study semantic biases in Machine Translation of nominal and verbal words in five different languages.
Outcome:	The proposed benchmark tests state-of-the-art machine translation systems against the new test bed and provides a statistical and linguistic analysis of the results.

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Yihong Tang, Bo Wang, Miao Fang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)

Challenge:	Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories.
Approach:	They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories.
Outcome:	The proposed model improves on Chinese and English datasets.

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Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing (2022.acl-long)

Challenge:	Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning.
Approach:	They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods.
Outcome:	The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages.

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Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Sebastian Schwemer, Anders Søgaard

Korean Language Modeling via Syntactic Guide (2022.lrec-1)

Challenge:	Using pre-trained language models, we evaluate performance group disparities while none of these techniques guarantee fairness, nor consistently mitigate group disparity.
Approach:	They present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.
Outcome:	The proposed methods show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparity.

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Hyeondey Kim, Seonhoon Kim, Inho Kang, Nojun Kwak, Pascale Fung

A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation (C18-1)

Challenge:	Existing research on pre-trained language models focuses on widely-used languages . however, not every language can benefit from such models due to computational resources .
Approach:	They propose to build a pre-trained language model that understands the linguistic phenomena in the target language with low resources.
Outcome:	The proposed model improves the performance of Korean language understanding tasks.

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Zongsheng Wang, Yunzhi Bai, Bowen Wu, Zhen Xu, Zhuoran Wang, Baoxun Wang

BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation (2022.acl-long)

Challenge:	Existing methods to generate responses using beam search focus on current optimal results.
Approach:	They propose a beam search method that uses a Prospective-Performance Network to predict the future reward of a partially-generated response.
Outcome:	The proposed method can increase the quality and diversity of generated responses with high inference efficiency.

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Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš

Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)

Challenge:	Existing benchmarks for OIE are incomplete and do not include all acceptable variants of the same fact.
Approach:	They introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.
Outcome:	The proposed framework is based on fact synsets, clusters, and standardized benchmarks.

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Mohan Zhang, Luchen Tan, Zihang Fu, Kun Xiong, Jimmy Lin, Ming Li, Zhengkai Tu

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful .
Approach:	They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior .
Outcome:	The proposed technique is language agnostic and tested in English and Chinese.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence Moss

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (2023.emnlp-main)

Challenge:	Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets.
Approach:	They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations.
Outcome:	The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics.

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Nam Nguyen, Thang Phan, Duc-Vu Nguyen, Kiet Nguyen

Textual Enhanced Contrastive Learning for Solving Math Word Problems (2022.findings-emnlp)

Challenge:	English and Chinese have seen the strong development of transformer-based language models for natural language processing tasks.
Approach:	They present a monolingual pre-trained language model for Vietnamese social media texts . they explore emotion recognition, hate speech detection, sentiment analysis, spam reviews detection .
Outcome:	The proposed model outperforms the existing models on Vietnamese social media tasks with fewer parameters.

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Yibin Shen, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi

JGLUE: Japanese General Language Understanding Evaluation (2022.lrec-1)

Challenge:	Recent studies show that current models rely on shallow heuristics to predict solutions . a textual Enhanced Contrastive Learning framework enforces the models to distinguish semantically similar examples while holding different mathematical logic.
Approach:	They propose a textual Enhanced Contrastive Learning framework which enforces models to distinguish semantically similar examples while holding different mathematical logic.
Outcome:	The proposed framework improves on benchmark and challenge datasets in English and Chinese.

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Kentaro Kurihara, Daisuke Kawahara, Tomohide Shibata

Challenge:	There is no benchmark for Japanese to evaluate and analyze NLU ability from different perspectives.
Approach:	They build a Japanese NLU benchmark from scratch without translation to measure general NLU ability in Japanese.
Outcome:	a Japanese NLU benchmark is built from scratch without translation to measure general NLU ability in Japanese.

Segmentation-free compositional n-gram embedding (N19-1)

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Geewook Kim, Kazuki Fukui, Hidetoshi Shimodaira

Representation Learning

Challenge:	Existing word embedding models depend on word segmentation, but this method is difficult when corpora written in noisy or unsegmented languages.
Approach:	They propose a new method that models words, phrases and sentences seamlessly without word segmentation.
Outcome:	The proposed method is very effective for noisy corpora written in unsegmented languages such as Chinese and Japanese.

Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases (2024.lrec-main)

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Yuqi Liu, Guanyi Chen, Kees van Deemter

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)

Challenge:	linguists have suggested that some languages are "cooler" than others because of their contexts.
Approach:	They propose to omit plurality and definiteness markers in Chinese noun phrases . they build a corpus of Chinese NPs accompanied by its context .
Outcome:	The proposed model predicts the plurality and definiteness of Chinese noun phrases (NPs) it shows that speakers drop plurality markers very frequently, and that they are more likely to drop pronouns .

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Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He

Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)

Challenge:	Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks.
Approach:	They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese.
Outcome:	The proposed model improves the few-shot table understanding in Chinese.

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Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson

Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)

Challenge:	Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings.
Approach:	They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset.
Outcome:	The proposed model trains on Chinese and English natural language inference datasets.

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Longyue Wang, Zhaopeng Tu, Andy Way, Qun Liu

Multilingual Constituency Parsing with Self-Attention and Pre-Training (P19-1)

Challenge:	Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics .
Approach:	They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach.
Outcome:	The proposed approach improves translation performance and accuracy of DP predictions.

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Nikita Kitaev, Steven Cao, Dan Klein

CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)

Challenge:	a range of pre-training conditions can be used for constituency parsing, but large model sizes make it expensive to train separate models for each language.
Approach:	They compare the benefits of no pre-training, fastText, ELMo, and BERT for English . they also find that pre- training is beneficial across all 11 languages tested .
Outcome:	The proposed model outperforms fastText, ELMo, and BERT for English . but large model sizes make it expensive to train separate models for each language .

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Yuying Zhu, Guoxin Wang

ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations (2024.emnlp-main)

Challenge:	Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.
Approach:	They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts.
Outcome:	The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets.

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Yunze Xiao, Yujia Hu, Kenny Choo, Roy Lee

CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)

Challenge:	Existing large language models struggle with systematically perturbed data designed to evade detection mechanisms.
Approach:	They propose a large language model with homophonic substitutions and emoji transformations to test their models' robustness against cloaking perturbations.
Outcome:	The proposed model underperforms in detecting offensive content when perturbations are applied to Chinese language datasets.

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Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen

Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study (2023.emnlp-main)

Challenge:	Existing multilingual pre-trained language models do not perform well on some low-resource languages.
Approach:	They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets .
Outcome:	The proposed model outperforms baseline models on various classification tasks.

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Freddy Heppell, Kalina Bontcheva, Carolina Scarton

SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

Challenge:	a network of doppelganger websites (impersonating genuine news sites) was discovered in 2022 . a novel dataset enables studies of disinformation networks and the training of NLP tools for disinformation detection.
Approach:	They analyze two hitherto unstudied sites sharing state-backed disinformation . they perform cross-site topic clustering and perform linguistic and temporal analysis .
Outcome:	The proposed dataset includes 14,053 articles, annotated with each language version, and additional metadata such as links and images.

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Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)

Challenge:	Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach:	They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome:	The proposed model outperforms existing models in symbolic song composition tasks.

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Boyang Li, Hongzhe Shou, Yuanyuan Liang, JingBin Zhang, Fang Zhou

A Deep Analysis of the Impact of Multiword Expressions and Named Entities on Chinese-English Machine Translations (2024.findings-emnlp)

Challenge:	Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans.
Approach:	They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance .
Outcome:	The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations.

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Huacheng Song, Hongzhi Xu

Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis (L18-1)

Challenge:	a study on the impact of multiword expressions and multiword named entities (NEs) on the performance of Chinese-English machine translation systems is presented.
Approach:	They propose to use Chinese multiword expressions and multiword named entities (NEs) to evaluate machine translation performance.
Outcome:	The proposed methods show that Chinese-English machine translation systems perform significantly worse on Chinese sentences with most kinds of MWEs and NEs.

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Lung-Hao Lee, Yuen-Hsien Tseng, Li-Ping Chang

DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks (2022.naacl-main)

Challenge:	Annotated learner corpus is valuable for research in second language acquisition, foreign language teaching, and contrastive interlanguage analysis.
Approach:	They construct a TOCFL learner corpus and use it for Chinese grammatical error diagnosis.
Outcome:	The constructed corpus is available to the public and will be used for shared tasks on Chinese grammatical error diagnosis.

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Lin Tian, Xiuzhen Zhang, Jey Han Lau

Challenge:	Social media rumours can cause significant economic and social disruption.
Approach:	They propose a rumour detection algorithm that leverages transformers and graph attention networks to jointly model social media conversations and the network of users who engaged in them.
Outcome:	The proposed algorithm produces superior performance over four widely used benchmark rumour datasets in English and Chinese.

M2QA: Multi-domain Multilingual Question Answering (2024.findings-emnlp)

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Leon Engländer, Hannah Sterz, Clifton Poth, Jonas Pfeiffer, Ilia Kuznetsov, Iryna Gurevych

The Multilingual Amazon Reviews Corpus (2020.emnlp-main)

Challenge:	Language varies along several axes, most importantly, language instance and domain . lack of evaluation datasets prevents transfer of NLP systems to non-dominant languages .
Approach:	They propose a multi-domain multilingual question answering benchmark to explore cross-lingual cross-domain performance of fine-tuned models and state-of-the-art LLMs.
Outcome:	The proposed benchmark compared 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing.

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Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith

CSSWiki: A Chinese Sentence Simplification Dataset with Linguistic and Content Operations (2024.lrec-main)

Challenge:	The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 .
Approach:	They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings.
Outcome:	The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings.

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Fengkai Liu, John S. Y. Lee

A Pilot Study for Chinese SQL Semantic Parsing (D19-1)

Challenge:	Existing datasets for sentence simplification focus on English, but limited in Chinese . SS tasks are aimed at improving readability and making sentences more accessible for readers .
Approach:	They propose an open-source dataset for Chinese sentence simplification based on Wikipedia . they analyze differences in annotation scheme and data statistics between datasets .
Outcome:	The proposed dataset contains 1.6k source sentences paired with their simplified versions.

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Qingkai Min, Yuefeng Shi, Yue Zhang

Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)

Challenge:	Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping.
Approach:	They propose to use a Chinese-based semantic parser to map natural language text into SQL.
Outcome:	The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping.

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Bich-Ngoc Do, Ines Rehbein

How to Make the Most of LLMs’ Grammatical Knowledge for Acceptability Judgments (2025.naacl-long)

Challenge:	Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser.
Approach:	They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies .
Outcome:	The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data.

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Yusuke Ide, Yuto Nishida, Justin Vasselli, Miyu Oba, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons (2024.naacl-long)

Challenge:	Conventional approaches compare sentence probabilities directly, but large language models (LLMs) provide nuanced evaluation methods using prompts and templates.
Approach:	They propose to derive acceptability judgments from large language models using prompts and templates to comprehensively evaluate their grammatical knowledge.
Outcome:	The proposed methods excel in different linguistic phenomena, suggesting they access different aspects of the LLMs’ grammatical knowledge.

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Takeshi Kojima, Itsuki Okimura, Yusuke Iwasawa, Hitomi Yanaka, Yutaka Matsuo

Induction Networks for Few-Shot Text Classification (D19-1)

Challenge:	Existing decoder-based pre-trained language models demonstrate excellent multilingual capabilities, but it is unclear how they handle multilingualism.
Approach:	They propose to examine the neuron-level internal behavior of decoder-based PLMs by finding neurons that fire “uniquely for each language” within decoded PLM models.
Outcome:	The proposed models fire “uniquely for each language” and show that language-specific neurons are unique, with a slight overlap (5%) between languages.

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Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, Jian Sun

A Unified Generative Framework for Bilingual Euphemism Detection and Identification (2024.findings-acl)

Challenge:	Recent studies have used meta-learning to simulate the few-shot task . however, this sample-wise comparison may be severely disturbed by the various expressions in the same class.
Approach:	They propose a meta-learning-based induction network to learn a generalized class-wise representation of each class in a support set.
Outcome:	The proposed model outperforms existing state-of-the-art models on a sentiment and dialogue intent datasets.

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Yuxue Hu, Junsong Li, Tongguan Wang, Dongyu Su, Guixin Su, Ying Sha

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

Challenge:	Existing euphemism datasets are only domain-specific or language-specific.
Approach:	They propose a unified model to jointly conduct bilingual euphemism detection and identification tasks.
Outcome:	The proposed model is effective and provides a new reference standard for euphemism detection and identification.

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models (2024.acl-long)

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

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Qihang Ai, Jiafan Li, Jincheng Dai, Jianwu Zhou, Lemao Liu, Haiyun Jiang, Shuming Shi

Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)

Challenge:	Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning .
Approach:	They propose a new paradigm for interactive and instructional graph data understanding and reasoning . they first evaluate the capabilities of public VLMs in graph learning from multiple aspects .
Outcome:	The proposed model achieves an accuracy increase of 5%-15% compared to baseline models . the best-performing model achieve scores comparable to Gemini in GPT-asissted Evaluation .

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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak

Neural Legal Judgment Prediction in English (P19-1)

Challenge:	Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context.
Approach:	They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations .
Outcome:	The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say .

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Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras

Text Classification

Challenge:	Recent work on legal judgment prediction has focused on Chinese, but only feature-based models have been considered in English.
Approach:	They propose a hierarchical version of BERT which bypasses BERT’s length limitation.
Outcome:	The proposed model outperforms existing models in binary violation classification, multi-label classification and case importance prediction.

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations (2020.findings-emnlp)

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Shizhe Diao, Jiaxin Bai, Yan Song, Tong Zhang, Yonggang Wang

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)

Challenge:	Experimental results show that pre-trained text encoders can perform many NLP tasks with less resource.
Approach:	They propose a BERT-based Chinese text encoder enhanced by n-gram representations . they show reasonable performance when ZEN is trained on a small corpus .
Outcome:	The proposed encoder incorporates the comprehensive information of both the character sequence and words or phrases it contains.

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Jizhan Fang, Tianhe Lu, Yunzhi Yao, Ziyan Jiang, Xin Xu, Huajun Chen, Ningyu Zhang

ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)

Challenge:	CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models.
Approach:	They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models.
Outcome:	The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show .

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Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Ting Liu, Bing Qin, Yi Zheng, Baoxing Huai

Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)

Challenge:	Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift.
Approach:	They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks.
Outcome:	The proposed framework outperforms baselines on Chinese and English CCR datasets.

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Zhijun Wang, Xuebo Liu, Min Zhang

MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space (2024.emnlp-main)

Challenge:	Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector.
Approach:	They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters .
Outcome:	The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations .

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Yihong Tang, Bo Wang, Dongming Zhao, Jinxiaojia Jinxiaojia, Zhangjijun Zhangjijun, Ruifang He, Yuexian Hou

Universal Semantic Tagging for English and Mandarin Chinese (2021.naacl-main)

Challenge:	Personalized Dialogue Generation relies on external role data, which can be scarce and raise privacy concerns.
Approach:	They propose a framework to extract role information from dialogue history . they use persona codebook to represent roles in latent space and posterior distribution of role information .
Outcome:	The proposed framework can generalize across roles, even for unseen roles.

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Wenxi Li, Yiyang Hou, Yajie Ye, Li Liang, Weiwei Sun

Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations (2020.coling-main)

Challenge:	Existing approaches to generating semantic annotations for different languages are attracting more and more interest.
Approach:	They propose to extend Universal Semantic Tagging to Mandarin Chinese and evaluate its performance.
Outcome:	The proposed scheme is only tested in four Indo–European languages . accuracies are 92.7% and 94.6% for Chinese and English respectively .

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Sheng Liang, Philipp Dufter, Hinrich Schütze

Depression Detection in Clinical Interviews with LLM-Empowered Structural Element Graph (2024.naacl-long)

Challenge:	Prior work identifies a linear gender subspace and removes gender information by eliminating the subspace.
Approach:	They propose to use DensRay to obtain interpretable dense subspaces by applying it to attention heads and layers of BERT.
Outcome:	The proposed method performs on-par with prior approaches, but is more robust and preserves language model performance better.

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Zhuang Chen, Jiawen Deng, Jinfeng Zhou, Jincenzi Wu, Tieyun Qian, Minlie Huang

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)

Challenge:	Existing methods for assessing depression only capture part of relevant elements . scarcity of participant data constrains interview modeling due to privacy concerns .
Approach:	They propose a structural element graph (SEGA) that transforms clinical interviews into an expertise-inspired directed acyclic graph for comprehensive modeling.
Outcome:	The proposed model outperforms baseline methods and powerful LLMs on two real-world clinical datasets.

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Chenglei Si, Zhengyan Zhang, Yingfa Chen, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives (2026.acl-long)

Challenge:	Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises.
Approach:	They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications.
Outcome:	The proposed benchmarks are based on four different tasks and are designed to maximize diversity.

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Yu Wang, Emmanuele Chersoni, Chu-Ren Huang

Challenge:	a new study examines whether large language models acquire embodied cognition and cultural conventions from training data . demonstratives are a natural lens for evaluating linguistic phenomena that reflect cultural variation . aaron e. duan and j. nà: "the complexity of the language model is a major challenge for LLMs"
Approach:	They introduce demonstratives as a probe for grounded knowledge by analyzing 6,400 responses from 320 native speakers.
Outcome:	The proposed model fails to understand proximal–distal contrast and shows no cultural differences . the proposed model is a new probe for evaluating embodied cognition and cultural conventions .

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)

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Youquan Li, Miao Zheng, Fan Yang, Guosheng Dong, Bin Cui, Weipeng Chen, Zenan Zhou, Wentao Zhang

DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain (2024.lrec-main)

Challenge:	Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios.
Approach:	They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese.
Outcome:	The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios.

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Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant Dit Beaufils, Natalia Grabar, Béatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

Challenge:	Existing benchmarks for pre-trained language models are limited to only a few languages . a limited number of tasks are evaluated on non-standardized protocols .
Approach:	They propose to aggregate diverse downstream tasks into a benchmark to assess PLMs' qualities . they evaluate 8 pre-trained masked language models on general and biomedical-specific data .
Outcome:	The proposed benchmark assesses pre-trained language models on 20 diversified tasks.

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi (2020.coling-main)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Rubungo Andre Niyongabo, Qu Hong, Julia Kreutzer, Li Huang

Position Offset Label Prediction for Grammatical Error Correction (2022.coling-1)

Challenge:	low-resource African languages are traditionally left behind because of the lack of well-annotated data and effective preprocessing.
Approach:	They propose two news datasets for multi-class classification of news articles in two low-resource African languages.
Outcome:	The proposed datasets show that training embeddings on the higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi.

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Xiuyu Wu, Jingsong Yu, Xu Sun, Yunfang Wu

Cross-Lingual Training for Automatic Question Generation (P19-1)

Challenge:	Experimental results show that our proposed POL-Pc framework improves baseline models and yields consistent performance gain over various data augmentation methods.
Approach:	They propose a position offset label prediction subtask to integrate correction editing operations into a unified framework.
Outcome:	The proposed model outperforms baseline models on Chinese, English and Japanese datasets by a wide margin.

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Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi

Cross-Lingual Transfer

Challenge:	Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive.
Approach:	They propose to reuse available large QG dataset in a secondary language to learn a QG model for a primary language.
Outcome:	The proposed model outperforms baseline models in Hindi and Chinese.

Empirical Study of Zero-Shot NER with ChatGPT (2023.emnlp-main)

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Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang, Zuozhu Liu, Hongwei Wang

Low-Resource NLP

Challenge:	Large language models (LLMs) have been a key component of natural language processing (NLP) .
Approach:	They propose to decompose the NER task into simpler subproblems by labels and propose a syntactic augmentation strategy to stimulate model's intermediate thinking.
Outcome:	The proposed methods achieve remarkable improvements for zero-shot NER across seven benchmarks, including Chinese and English datasets.

Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario (2022.emnlp-main)

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Xiao Liu, Yansong Feng, Jizhi Tang, Chengang Hu, Dongyan Zhao

Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading (2020.coling-main)

Challenge:	Existing models fail to learn and use culinary knowledge in a compositional way, argues a new study.
Approach:	They propose a task that asks models to modify a base recipe according to the change of an ingredient.
Outcome:	The proposed model can perform compositional generalization in a realistic setting . existing models have difficulties in modifying ingredients while preserving original style .

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Feng Jiang, Xiaomin Chu, Peifeng Li, Fang Kong, Qiaoming Zhu

TWBias: A Benchmark for Assessing Social Bias in Traditional Chinese Large Language Models through a Taiwan Cultural Lens (2024.findings-emnlp)

Challenge:	Existing methods on discourse parsing in English suffer from long discourse units and fewer explicit connectives.
Approach:	They propose to use two reading modes to construct Chinese paragraph level discourse trees.
Outcome:	The proposed model outperforms baselines on Chinese discourse trees.

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Hsin-Yi Hsieh, Shih-Cheng Huang, Richard Tsai

CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)

Challenge:	Large language models have shown remarkable capabilities in natural language processing, but concerns about social bias amplification remain.
Approach:	They propose a social bias evaluation benchmark for Traditional Chinese LLMs that integrates chat templates and diverse prompts for comprehensive bias assessment.
Outcome:	The proposed model incorporates chat templates and diverse prompts for comprehensive bias assessment focusing on Taiwan's cultural context and prioritizing gender and ethnicity bias evaluation.

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Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin, Angela Fan

Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)

Challenge:	Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English.
Approach:	They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English.
Outcome:	The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop.

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Chen Jia, Yuefeng Shi, Qinrong Yang, Yue Zhang

EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions (2025.findings-acl)

Challenge:	Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER.
Approach:	They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT .
Outcome:	The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors.

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Zheheng Luo, Chenhan Yuan, Qianqian Xie, Sophia Ananiadou

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)

Challenge:	Recent advances in Large Language Models (LLMs) show their potential in accurately answering biomedical questions, yet current healthcare benchmarks primarily assess knowledge mastered by medical doctors, neglecting other essential professions.
Approach:	They evaluated 17 LLMs including proprietary and open-source models and found they struggled with specialized fields and alternative medicine.
Outcome:	The examinations for medical PErsonnel in Chinese (EMPEC) features 157,803 exam questions across 124 subjects and 20 healthcare professions.

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Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu

STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)

Challenge:	Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs.
Approach:	They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary .
Outcome:	The proposed method achieves 13.64 55.53% accuracy between English and four distant languages.

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Zewen Bai, Liang Yang, Shengdi Yin, Junyu Lu, Jingjie Zeng, Haohao Zhu, Yuanyuan Sun, Hongfei Lin

Ethical NLP

Challenge:	Existing studies on Chinese hate speech detection lack span-level fine-grained annotations.
Approach:	They construct a Span-level target-aware Toxicity Extraction dataset and evaluate existing models for Chinese hateful slang.
Outcome:	The proposed dataset is the first span-level Chinese hate speech dataset and evaluates the ability of existing models to understand hate semantics.

Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English (2022.emnlp-main)

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Xiaohan Zhang, Shaonan Wang, Nan Lin, Chengqing Zong

Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations (2020.emnlp-main)

Challenge:	Existing studies have shown that the brain builds hierarchical syntactic structures, but it is unknown whether they are universal across languages.
Approach:	They analyze the working memory requirements when applying parsing strategies to two languages: Chinese and English.
Outcome:	The proposed method shows that the brain adopts parsing strategies with less memory load according to different language structures.

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Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong

Linking, Searching, and Visualizing Entities in Wikipedia (L18-1)

Challenge:	Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Approach:	They propose to use an encoder-decoder neural framework to continue the context with a quotation via language generation to capture latent topics, interactions with the dialogue history, and coherence to the existing contents.
Outcome:	The proposed model outperforms state-of-the-art models on two large-scale datasets in English and Chinese and shows that topic, interaction, and query consistency are helpful to learn how to quote in online conversations.

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Marcus Klang, Pierre Nugues

Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)

Challenge:	Existing systems to extract, index, search, and visualize entities in Wikipedia are not strings, but unique identifiers from Wikidata.
Approach:	They propose a system to extract, index, search, and visualize entities in Wikipedia . they use a document model to store linguistic annotations and a string matching engine .
Outcome:	The proposed system achieves CEAFm scores of 70.0 on English, 64.4 on Chinese, and 66.5 on Spanish.

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Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan

Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language We Prompt Them in (2024.lrec-main)

Challenge:	Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance.
Approach:	They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance.
Outcome:	The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets.

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Utkarsh Agarwal, Kumar Tanmay, Aditi Khandelwal, Monojit Choudhury

A Corpus for Multilingual Document Classification in Eight Languages (L18-1)

Challenge:	Ethical reasoning is a crucial skill for Large Language Models (LLMs). However, moral values are not universal, but rather influenced by language and culture.
Approach:	They extend the study of ethical reasoning of LLMs by (CITATION) to a multilingual setup using six languages: English, Spanish, Russian, Chinese, Hindi, and Swahili.
Outcome:	The proposed model is based on a multilingual setup in English, Spanish, Russian, Chinese, Hindi, and Swahili.

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Holger Schwenk, Xian Li

A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)

Challenge:	a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources.
Approach:	They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset .
Outcome:	The proposed subset of the Reuters corpus has balanced class priors for eight languages.

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Xinpeng Liu, Bing Xu, Muyun Yang, Hailong Cao, Conghui Zhu, Tiejun Zhao, Wenpeng Lu

Low-Resource NLP

Challenge:	Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs.
Approach:	They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation .
Outcome:	The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets.

TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)

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Junnan Zhu, Min Xiao, Yining Wang, Feifei Zhai, Yu Zhou, Chengqing Zong

When Generative Adversarial Networks Meet Sequence Labeling Challenges (2024.emnlp-main)

Challenge:	Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability.
Approach:	They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs.
Outcome:	The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese .

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Yu Tong, Ge Chen, Guokai Zheng, Rui Li, Jiang Dazhi

A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora (L18-1)

Challenge:	Existing approaches for sequence labeling use a feature extractor and sequence tagger . a recent study shows that SLGAN is versatile and highly effective .
Approach:	They propose a framework that harnesses the capabilities of Generative Adversarial Networks to address sequence labeling challenges.
Outcome:	The proposed framework exhibits strong adaptability to various sequence labeling tasks.

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Pierre Zweigenbaum, Serge Sharoff, Reinhard Rapp

Chinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion (2025.coling-main)

Challenge:	BUCC Shared Task aims to extract parallel sentences from comparable corporad . resulting corpus contains about 3.5 million distinct sentences in english, french, german, Russian, and Chinese .
Approach:	They present challenges faced to build a parallel sentences dataset from comparable corporad . they emphasize issues faced to include Chinese as one of the languages .
Outcome:	The 2017 BUCC Shared Task was a first for this task . the dataset contains 3.5 million sentences in English, French, German, Russian, and Chinese .

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Xusheng Yang, Jincai Yang, Xiao Li

A Pilot Study on Dialogue-Level Dependency Parsing for Chinese (2023.findings-acl)

Challenge:	Existing methods for classification of reading difficulty of texts are insufficiently trained and lack of linguistic features.
Approach:	They propose a method that combines adaptive pre-training with feature fusion to capture different text difficulties and an interactive attention mechanism to integrate linguistic and deep features.
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on Chinese textbook dataset and can be applied to other languages.

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Gongyao Jiang, Shuang Liu, Meishan Zhang, Min Zhang

Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore’s Low-Resource Languages (2025.emnlp-main)

Challenge:	Dialogue-level dependency parsing has received insufficient attention, especially for Chinese.
Approach:	They propose a signal-based method to transform seen syntactic dependencies into unseen ones between elementary discourse units (EDUs) they apply single-view and multi-view data selection to access reliable pseudo-labeled instances.
Outcome:	The proposed method transforms seen syntactic dependencies into unseen ones between elementary discourse units (EDUs) the proposed method also provides reliable pseudo-labeled instances.

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Yujia Hu, Ming Shan Hee, Preslav Nakov, Roy Ka-Wei Lee

SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)

Challenge:	Large language models (LLMs) have transformed natural language processing, but their safety mechanisms remain under-explored in low-resource, multilingual settings.
Approach:	They propose a red-teaming approach to probe LLM vulnerabilities in Singapore's diverse linguistic context using a dataset and evaluation framework.
Outcome:	The proposed framework systematically probes LLM vulnerabilities in three real-world scenarios including Singlish, Chinese, Malay, and Tamil.

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Siamak Barzegar, Brian Davis, Manel Zarrouk, Siegfried Handschuh, Andre Freitas

A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection (2025.emnlp-main)

Challenge:	SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Approach:	This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Outcome:	The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.

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Chong Tian, Qirong Ho, Xiuying Chen

AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)

Challenge:	Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation.
Approach:	They propose a framework that implements an adversarial training paradigm by an agent symbolic learning optimization process rather than numerical updates.
Outcome:	The proposed framework generates sophisticated fake news that degrades state-of-the-art detection performance by 53.4% in Chinese and 34.2% in English on average.

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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang

Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)

Challenge:	Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment.
Approach:	They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references.
Outcome:	The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references.

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Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, Fandong Meng

Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)

Challenge:	Knowledge editing is a promising technique to adapt large language models to new knowledge without retraining from scratch.
Approach:	They propose to use a multilingual dataset to translate a large-scale cross-lingual synthetic dataset from English to Chinese and then to evaluate their performance in Chinese.
Outcome:	The proposed method can change model performance on several special cases without retraining from scratch.

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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth

SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (2025.coling-main)

Challenge:	Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes.
Approach:	They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender.
Outcome:	The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning.

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Qingpo Yang, Yakai Chen, Zihui Xu, Yu-ming Shang, Sanchuan Guo, Xi Zhang

L1-L2 Parallel Treebank of Learner Chinese: Overused and Underused Syntactic Structures (L18-1)

Challenge:	Existing work on cyberbullying detection in Chinese is underdeveloped due to the lack of comprehensive and reliable datasets.
Approach:	They propose to use Chinese social media sessions to analyze Chinese cyberbullying content to improve the quality of annotations.
Outcome:	The proposed dataset shows that it performs better than existing methods on Weibo and a major social media platform.

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Keying Li, John Lee

Sign Languages and the Online World Online Dictionaries & Lexicostatistics (L18-1)

Challenge:	Currently, the treebank consists of 600 L2 sentences and 697 L1 sentences.
Approach:	They propose to use "L1-L2 parallel treebanks" to facilitate analyses of learner language.
Outcome:	The proposed treebank consists of 600 L2 sentences and 697 L1 sentences.

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Shi Yu, Carlo Geraci, Natasha Abner

Text Clustering

Challenge:	Several online dictionaries documenting the lexicon of a variety of sign languages are available . methodological issues must be addressed regarding how these resources are used for research purposes.
Approach:	They propose a web-based tool for annotating the articulatory features of signs . they compare handshapes for four Asian SLs and handshape for the entire sample .
Outcome:	The proposed tool compares handshapes and handsights of Asian SLs with European, American, and Brazilian SL samples.

CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages (2025.coling-main)

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Wenhao Zhuang, Yuan Sun

CMMLU: Measuring massive multitask language understanding in Chinese (2024.findings-acl)

Challenge:	Existing multilingual models such as XLM-R support only approximately 100-200 languages, leaving nearly 7,000 low-resource languages untapped.
Approach:	They construct and open-source a dataset of four-language corpora obtained through machine translation into Chinese, Uyghur and Tibetan.
Outcome:	The proposed dataset includes two resource-rich languages and two low-resource languages.

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Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin

Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)

Challenge:	Existing large language models struggle to achieve an accuracy of even 60%, which is the pass mark for Chinese exams.
Approach:	They propose to use CMMLU to evaluate Chinese multilingual and Chinese LLMs in a comprehensive benchmark that covers various subjects and settings.
Outcome:	The proposed benchmark covers natural sciences, social sciences, engineering, and the humanities and aims to improve on existing models.

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Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, Feng Xia

MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)

Challenge:	Humor plays important role in human communication, which makes it important problem for natural language processing.
Approach:	They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset .
Outcome:	The proposed scheme gives scenarios of how humor arises in text . it contains key words that trigger humor, character relationship, scene, and humor categories .

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Donghai Zhang, SHuangtao Yang, Dong Xiaozheng, Wei Song, Bo Fu

MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi (2025.findings-emnlp)

Challenge:	Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings.
Approach:	They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian.
Outcome:	The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times.

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Anuj Kumar, Mohammed Faisal Sayed, Satyadev Ahlawat, Yamuna Prasad

SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)

Challenge:	Marathi is the third most widely spoken language in India with over 83 million native speakers . available Marath datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations.
Approach:	They propose to annotate Marathi sentences labeled with sentiment, emotion and a corresponding natural language justification.
Outcome:	The proposed dataset provides a benchmark for future research in multilingual and explainable NLP.

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Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, Nanyun Peng

RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs (2024.emnlp-main)

Challenge:	Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts.
Approach:	They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages.
Outcome:	The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring.

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John Dang, Arash Ahmadian, Kelly Marchisio, Julia Kreutzer, Ahmet Üstün, Sara Hooker

Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)

Challenge:	Preference optimization is a widely adopted post-training technique to align large language models with human preferences.
Approach:	They propose a method for generating multilingual feedback data to balance data coverage.
Outcome:	The proposed method achieves 54.4% win-rate against current state-of-the-art multilingual LLM in its parameter class and 69.5% win- rate or higher against widely used models like Gemma, Mistral and Llama 3.

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Yuhang He, Jianzhu Bao, Yang Sun, Bin Liang, Min Yang, Bing Qin, Ruifeng Xu

Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2025.acl-long)

Challenge:	Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.
Approach:	They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning.
Outcome:	The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines.

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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions.
Approach:	They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information .
Outcome:	The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities.

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection (2023.acl-long)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Chenye Zhao, Yingjie Li, Cornelia Caragea

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)

Challenge:	Recent advances in zero-shot stance detection are limited to English and Chinese . stance can provide useful information for important events such as policymaking and presidential elections.
Approach:	They present a Chinese dataset for zero-shot stance detection that is the first for ZSSD.
Outcome:	The proposed dataset is the first Chinese dataset for zero-shot stance detection.

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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)

Challenge:	Existing solutions for visual document understanding lack granularity of document textlines.
Approach:	They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts.
Outcome:	The proposed system performs better on various VDU tasks in English and Chinese.

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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Improved Neural Protoform Reconstruction via Reflex Prediction (2024.lrec-main)

Challenge:	Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency.
Approach:	They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task.
Outcome:	The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability.

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Liang Lu, Jingzhi Wang, David R. Mortensen

Challenge:	comparative method allows linguists to infer protoforms from their reflexes based on sound change . authors argue that this approach ignores one of the most important aspects of the comparative approach .
Approach:	They propose a comparative method that allows linguists to infer protoforms from their reflexes . they propose to use a system where candidate protoform from a reconstruction model are reranked by a reflex prediction model.
Outcome:	The comparative method surpasses state-of-the-art methods on Chinese and Romance datasets.

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic (2024.acl-long)

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Rishabh Bhardwaj, Duc Anh Do, Soujanya Poria

IndicFinNLP: Financial Natural Language Processing for Indian Languages (2024.lrec-main)

Challenge:	RESTA is a tool to perform LLM realignment towards safety, which gets compromised due to downstream task fine-tuning.
Approach:	They propose to add a safety vector to the weights of a compromised model by arithmetic and demonstrate its generalizability on three existing safety evaluation benchmarks and a multilingual benchmark dataset.
Outcome:	The proposed model reduces harmfulness of the model from 18.6% to 5.1% and from 9.2% to 1.5% in parameter-efficient and full fine-tuning while maintaining most of the system's performance on the task.

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Sohom Ghosh, Arnab Maji, Aswartha Narayana, Sudip Kumar Naskar

Challenge:	IndicFinNLP is a collection of 9 datasets relating to FinNLP for three Indian languages.
Approach:	They propose to use financial NLP to detect exaggerated numerals in financial texts written in Hindi, Bengali, and Telugu.
Outcome:	The proposed framework detects exaggerated numerals in financial texts written in Hindi, Bengali, and Telugu.

IndicSpeech: Text-to-Speech Corpus for Indian Languages (2020.lrec-1)

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Nimisha Srivastava, Rudrabha Mukhopadhyay, Prajwal K R, C V Jawahar

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

Challenge:	India has 22 languages, each of them being spoken by over a million people . the current state of the art text-to-speech systems for Indian languages are lacking in the multimedia domain .
Approach:	They propose to train a state-of-the-art TTS system for Hindi, Malayalam and Bengali and publish the results.
Outcome:	The proposed system trains neural text-to-speech systems for Hindi, Malayalam and Bengali and makes them publicly available.

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

An LLM-based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset Alzheimer’s Disease (LOAD) Stagings Especially in Chinese and English-speaking Populations (2025.findings-emnlp)

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

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Yang Han, Jacqueline C.k. Lam, Victor O.k. Li, Lawrence Y. L. Cheung

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Challenge:	Existing approaches struggle with temporal-spatial challenges in capturing subtle linguistic shifts across different disease stages.
Approach:	They propose a large language model-driven T-S fusion framework that integrates multilingual LLMs, contrastive learning and interpretable marker discovery to revolutionize late onset AD detection.
Outcome:	The proposed framework achieves state-of-the-art performance in late onset AD detection while enabling cross-linguistic diagnostics.

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

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Chun Lin, Ying-Jia Lin, Chia-Jen Yeh, Yi-Ting Li, Ching Yang, Hung-Yu Kao

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models (2024.findings-emnlp)

Challenge:	Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context.
Approach:	They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework.
Outcome:	The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine.

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Qin Zhang, Sihan Cai, Jiaxu Zhao, Mykola Pechenizkiy, Meng Fang

Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)

Challenge:	Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration.
Approach:	They propose a dataset to evaluate LLMs' ability to handle ambiguity in Chinese by using a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference.
Outcome:	The CHAmbi dataset comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities.

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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai

ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)

Challenge:	Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors.
Approach:	They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability.
Outcome:	The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections.

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Ruihui Hou, Siyi Zhu, Ziyue Huai, Guangya Yu, Yongqi Fan, ChunMing Wang, Tong Ruan

PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes (2025.emnlp-main)

Challenge:	Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time.
Approach:	They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time.
Outcome:	The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge.

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Zhijun Xu, Siyu Yuan, Yiqiao Zhang, Jingyu Sun, Tong Zheng, Deqing Yang

Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond (2023.emnlp-main)

Challenge:	Pun memes combine wordplay with visual elements to create humor, irony, or other rhetorical effects.
Approach:	They propose a benchmark to assess Chinese pun memes' processing capabilities across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response.
Outcome:	The proposed model can detect pun memes, analyze sentiments, and respond to chats, while ignoring homophone wordplay.

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Siyang Liu, Naihao Deng, Sahand Sabour, Yilin Jia, Minlie Huang, Rada Mihalcea

StruNRAG: Evaluation of OCR-Induced Structural Noise on RAG Robustness (2026.findings-acl)

Challenge:	Existing methods to generate text in mental health are limiting, but they are effective for many tasks.
Approach:	They propose a task-adaptive tokenizer that allows for the integration of task-specific tokens into the pre-trained model's tokenization step.
Outcome:	The proposed tokenization approach improves generation performance on psychological question-answering tasks in Chinese and English while using 60% fewer tokens.

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Mengna Gao, Dapeng Yin, Shuyue Zhu, Bingxuan Hou, Zhanpeng Ni, Junli Wang

Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)

Challenge:	Existing evaluations of RAG systems ignore structural noise, authors say . complex layouts can cause OCR failures and disrupt semantic flow of text . advanced LLMs demonstrate robustness against local noise, but struggle to maintain reasoning capabilities under severe structural disruption that fragments global context.
Approach:	They propose a benchmark to evaluate RAG robustness against OCR-induced structural perturbations.
Outcome:	The proposed benchmark systematically injects three categories of real-world structural noise into a bilingual dataset of 2,132 question-answer pairs . results show that advanced LLMs demonstrate robustness against local noise, but struggle to maintain reasoning capabilities under severe structural disruption .

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Florian Eichin, Carolin M. Schuster, Georg Groh, Michael A. Hedderich

NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation (2023.emnlp-main)

Challenge:	Existing methods for topic modeling fail to scale to large datasets or assume one topic per document.
Approach:	They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese .
Outcome:	The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets.

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Oliver Li, Mallika Subramanian, Arkadiy Saakyan, Sky CH-Wang, Smaranda Muresan

Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence (2025.findings-acl)

Challenge:	Social norms fundamentally shape interpersonal communication.
Approach:	They propose a human-in-the-loop pipeline to synthesize a bilingual dyadic dialogue dataset with turn-by-turn annotations of social norms for Chinese and American cultures.
Outcome:	The proposed dataset is high-quality through human evaluation and compares with existing models.

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Linyang He, Ercong Nie, Helmut Schmid, Hinrich Schuetze, Nima Mesgarani, Jonathan Brennan

Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)

Challenge:	Existing evaluations of Large Language Models (LLMs) reflect statistical rules that may not accurately represent LLMs’ true linguistic competence.
Approach:	They propose a method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers.
Outcome:	The proposed method combines minimal pair and diagnostic probing to analyze activation patterns across model layers.

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Sixing Wu, Jiong Yu, Tianshi Che, Yang Zhou, Wei Zhou

Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)

Challenge:	Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue.
Approach:	They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages.
Outcome:	The proposed model improves the current dialogue generation while reducing the hallucination issue.

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Yue Wang, Hua Zheng, Yaqi Yin, Hansi Wang, Qiliang Liang, Yang Liu

Visual Data in NLP

Challenge:	Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods.
Approach:	They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks.
Outcome:	The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word .

Think Globally, Group Locally: Evaluating LLMs Using Multi-Lingual Word Grouping Games (2025.emnlp-main)

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César Guerra-Solano, Zhuochun Li, Xiang Lorraine Li

ML-Promise: A Multilingual Dataset for Corporate Promise Verification (2025.emnlp-main)

Challenge:	Large language models exhibit biases in reasoning abilities due to linguistic modality, even with similar content.
Approach:	They propose a task inspired by the New York Times Connections: GlobalGroup that evaluates large language models in an abstract reasoning task across several languages.
Outcome:	The proposed task evaluates models across multiple languages in English, Spanish, Chinese, Hindi, and Arabic.

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Yohei Seki, Hakusen Shu, Anaïs Lhuissier, Hanwool Lee, Juyeon Kang, Min-Yuh Day, Chung-Chi Chen

Multi-Tiered Cantonese Word Segmentation (2024.lrec-main)

Challenge:	Promises shape perceptions and drive decisions, but verification of their fulfillment is difficult due to complexity and volume of commitments . authors propose a new approach to verifying promises in environmental, social, and governance reports . complexity of promises, complexity of evidence, difficulty in verifying their fulfillment a pressing need for new approaches .
Approach:	They propose a multilingual dataset that includes English, French, Chinese, Japanese, and Korean . they propose ML-Promise to facilitate in-depth verification of corporate promises .
Outcome:	The proposed approach includes promise identification, evidence assessment, and evaluation of timing for verification in multiple languages.

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Charles Lam, Chaak-ming Lau, Jackson L. Lee

SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms (2025.findings-acl)

Challenge:	Existing work on word segmentation for Chinese does not have conventional word boundaries as English does.
Approach:	They propose a linguistically motivated, multi-tiered word segmentation system for Cantonese . they propose linguisticly motivated, linguistic-motivated system that can cater to different needs .
Outcome:	The proposed system can be adapted to Cantonese corpus data.

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Yuki Hou, Haruki Tamoto, Qinghua Zhao, Homei Miyashita

Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)

Challenge:	Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations.
Approach:	They propose a method that combines temporal triggers and synaptic-like stimulus propagation to identify relevant dialogue histories.
Outcome:	The proposed approach improves on four datasets of English, Chinese and Japanese compared to state-of-the-art retrieval methods by 14.66% points.

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Wei Liu, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li, Yajun Du

IndicSynth: A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages (2025.acl-long)

Challenge:	Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese.
Approach:	They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference.
Outcome:	The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics.

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Divya V Sharma, Vijval Ekbote, Anubha Gupta

NLPre: A Revised Approach towards Language-centric Benchmarking of Natural Language Preprocessing Systems (2024.lrec-main)

Challenge:	Recent advances in synthetic speech generation technology have enabled the generation of high-quality synthetic (fake) speech that emulates human voices.
Approach:	They propose a dataset that contains 4,000 hours of synthetic speech from 989 target speakers for 12 low-resourced Indian languages.
Outcome:	The proposed dataset contains 4,000 hours of synthetic speech from 989 target speakers, including 456 females and 533 males for 12 low-resourced Indian languages.

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Martyna Wiącek, Piotr Rybak, Łukasz Pszenny, Alina Wróblewska

Re-Evaluating Evaluation for Multilingual Summarization (2024.emnlp-main)

Challenge:	GLUE benchmarking system enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance.
Approach:	They propose a language-centric benchmarking system that enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance.
Outcome:	The proposed system is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark.

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Jessica Forde, Ruochen Zhang, Lintang Sutawika, Alham Aji, Samuel Cahyawijaya, Genta Winata, Minghao Wu, Carsten Eickhoff, Stella Biderman, Ellie Pavlick

Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification (2026.acl-long)

Challenge:	Existing studies have shown that automated evaluation approaches correlate with human ratings in English, but this is unclear for other languages.
Approach:	They construct a small-scale pilot dataset containing article-summary pairs and human ratings in English, Chinese and Indonesian to measure the strength of summaries.
Outcome:	The results show that standard metrics are unreliable measures of quality in Chinese and Indonesian.

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Jinhong Jeong, Junghun Park, Youngjae Yu

Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models (2025.findings-acl)

Challenge:	Existing large language model-based readability control methods rely on pre-labeled sentence corpora and primarily target English.
Approach:	They propose a framework for adaptive multilingual text simplification without parallel corpora supervision that integrates three reward modules: vocabulary coverage, semantic preservation, and coherence.
Outcome:	The proposed framework achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency compared to stronger LLMs.

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Wenqing Wang, Mingqi Gao, Xinyu Hu, Xiaojun Wan

Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba) (2025.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) context windows have enabled them to process inputs over 100K tokens and generate outputs of up to 10K token.
Approach:	They propose a multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels and an annotated fiction dataset.
Outcome:	The proposed framework incorporates ten metrics across the Macro, Meso, and Micro levels and is based on a human-human-AI dataset.

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Ruiqi He, Yushu He, Longju Bai, Jiarui Liu, Zhenjie Sun, Zenghao Tang, He Wang, Hanchen Xia, Rada Mihalcea, Naihao Deng

Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment (2026.acl-long)

Challenge:	Existing studies on humor in non-English languages lack culturally nuanced humor in other languages.
Approach:	They construct a Chinese humor explanation dataset using a reddit-like platform . they test ten LLMs and find they are significantly better than existing LLM models .
Outcome:	The proposed dataset is the first and largest Chinese humor explanation dataset.

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Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Roy Ka-Wei Lee

Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)

Challenge:	Current alignment paradigms treat "human values" as a monolithic entity, ignoring the fact that many societies are a mosaic of diverse subgroups with distinct and sometimes conflicting values, preferences, and norms.
Approach:	They examine whether Large Language Models can emulate distinct cultural values of subgroups . they use a global value survey to examine the value landscape of a multicultural society .
Outcome:	The proposed model improves on unseen, out-of-distribution subgroups by 17.4% . the model widens the disparity between subgroup groups when measured by distance-aware metrics.

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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab

C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations (2025.emnlp-main)

Challenge:	Despite advances in machine translation, domain-specific terminology translation remains challenging.
Approach:	They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation.
Outcome:	The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores.

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Chengqian Ma, Wei Tao, Steven Y. Guo

Speech & Audio in NLP

Challenge:	Recent developments in spoken dialogue models have created a gap in understanding their effectiveness in comprehending and emulating human conversations.
Approach:	They present a benchmark dataset which comprises 1,079 instances in English and Chinese to examine their effectiveness in emulating human conversations.
Outcome:	The proposed model outperforms existing models in English and Chinese by using an LLM-based evaluation method that closely aligns with human judgment.

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Exploiting Phonetics and Glyph Representation at Radical-level for Classical Chinese Understanding (2025.findings-acl)

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

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Junyi Xiang, Maofu Liu

Quite Good, but Not Enough: Nationality Bias in Large Language Models - a Case Study of ChatGPT (2024.lrec-main)

Challenge:	Existing approaches in classical Chinese understanding have integrated heterogeneous linguistic knowledge, spanning lexicalsemantic relationships.
Approach:	They propose a radical-level phonetics and glyph representation enhanced Chinese model with powerful fine-grained semantic modeling capabilities.
Outcome:	The proposed model establishes robust representations through rules-based radical decomposition and bype pair encoder (BPE) based radical aggregated for structural pattern recognition, phonetic-glyph semantic mapping, and dynamic semantic fusion.

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Shucheng Zhu, Weikang Wang, Ying Liu

Assessing the Role of Data Quality in Training Bilingual Language Models (2025.findings-emnlp)

Challenge:	Nationality is a key demographic element that enhances the performance of large language models, but it has received less scrutiny regarding inherent biases.
Approach:	They investigated nationality bias in ChatGPT, a large language model for text generation.
Outcome:	The proposed model generates 4,680 discourses about nationality in Chinese and English, with 195 countries, 4 temperature settings, and 3 prompt types.

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Skyler Seto, Maartje Ter Hoeve, Maureen de Seyssel, David Grangier

CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs’ Cultural Knowledge Through Human-AI Red-Teaming (2025.acl-long)

Challenge:	a recent study shows that adding more languages can degrade performance for some languages while improving others.
Approach:	They propose a data filtering strategy to select high-quality bilingual training data with only high quality English data.
Outcome:	The proposed approach improves bilingual model performance by 2–4% and reduces bilingual models performance gaps to 1%.

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Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, Yejin Choi

SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (2024.lrec-main)

Challenge:	CulturalBench is a set of 1,696 human-written and human-verified questions to assess LMs’ cultural knowledge covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru.
Approach:	They construct a set of 1,696 human-written and human-verified questions to assess LMs' cultural knowledge, covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru.
Outcome:	The proposed model outperforms other models across cultures, while underperforming on questions related to North Africa, South America and Middle East.

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Tan Yue, Xuzhao Shi, Rui Mao, Zonghai Hu, Erik Cambria

Under the Shadow of Babel: How Language Shapes Reasoning in LLMs (2025.findings-emnlp)

Challenge:	Sarcasm is an implicit form of sarcasm, involving an intended meaning that contradicts the literal expression . human use conflict between factual information and a statement as cues to detect sarcasm . sarkasmatic analysis is challenging due to its implicit nature .
Approach:	They propose a multimodal sarcasm detection dataset that uses multiple modalities to detect sarcasm.
Outcome:	The proposed model improves on previous models based on a single label . human sarcasm cannot be detected using a unified label across multiple modalities .

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Chenxi Wang, Yixuan Zhang, Lang Gao, Zixiang Xu, Zirui Song, Yanbo Wang, Xiuying Chen

Linguistics & Cognitive NLP

Challenge:	linguistic relativity suggests that the structure of language shapes cognitive patterns . large language models internalize the habitual logical structures embedded in different languages, authors say .
Approach:	a study introduces a bilingual dataset for causal reasoning in Chinese and English.
Outcome:	a new study shows that large language models internalize reasoning biases shaped by language . the model internalizes language-specific preferences and rigidly applies them to atypical inputs, the study shows .

FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation (2026.acl-long)

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Yubo Shan, Kun Zhang, Qiming Xu, Liping Cao, Yingying Cao, Jian Zhang, Yu Wang, Jingyuan Li, Yuanzhuo Wang

Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025.findings-emnlp)

Challenge:	Existing benchmarks lack effective mechanisms to evaluate factual consistency in interleaved image-text generation.
Approach:	They propose a benchmark dedicated to evaluating factual consistency in interleaved image-text generation.
Outcome:	The proposed framework outperforms existing evaluation methods in evaluating factual consistency in interleaved image-text generation.

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Muhammed Saeed, Shaina Raza, Ashmal Vayani, Muhammad Abdul-Mageed, Ali Emami, Shady Shehata

LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)

Challenge:	grammatical gender significantly influences image generation in text-to-image models . masculine grammatikal markers increase male representation to 73% on average . feminine grammatological markers increase female representation to 38% .
Approach:	They propose a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations.
Outcome:	The proposed benchmark examines words where grammatical gender contradicts stereotypical gender associations.

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Yi Jing, Zijun Yao, Hongzhu Guo, Lingxu Ran, Xiaozhi Wang, Lei Hou, Juanzi Li

FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning (2026.findings-acl)

Challenge:	Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
Approach:	They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders.
Outcome:	The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs .

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Xiao Li, Bolin Zhu, Kaiwen Shi, Sichen Liu, Yin Zhu, Yiwei Liu, Gong Cheng

Training-free LLM Merging for Multi-task Learning (2025.acl-long)

Challenge:	Existing datasets for numerical reasoning often lack explicit knowledge of formulas . current datasets do not provide process supervision information, resulting in incomplete reasoning .
Approach:	They propose a benchmark for formula-based numerical reasoning with 5,324 questions . they provide annotations in English and Chinese and a formula database as an external knowledge source .
Outcome:	The proposed model includes 5,324 questions requiring calculations grounded in external physics principles.

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Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao

Where the Cat Sat: A Multilingual Framework for Spatial Language Understanding (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing tasks.
Approach:	They propose a training-free method for unifying different specialized LLMs into a single model using model-wise and layer-wise pruning and scaling.
Outcome:	The proposed method outperforms existing merging techniques and surpasses models fine-tuned on combined datasets in most scenarios.

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Demian Inostroza, Ekaterina Vylomova, Charles Kemp, Mae Carroll, Wanchun Li, Meladel Mistica

Multilingual Dialogue Generation and Localization with Dialogue Act Scripting (2025.emnlp-main)

Challenge:	Existing work exhibits biases toward English and prepositional marking . Existing models are limited in understanding spatial relations across typologically diverse languages .
Approach:	They propose a multilingual framework and benchmark for spatial language understanding . they decompose spatial relations into surface elements and semantic components . their results suggest surface parsing does not entail spatial understanding - they argue .
Outcome:	The proposed framework and benchmark decomposes spatial relations into surface elements and semantic components.

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Justin Vasselli, Eunike Andriani Kardinata, Yusuke Sakai, Taro Watanabe

NormGenesis: Multicultural Dialogue Generation via Exemplar-Guided Social Norm Modeling and Violation Recovery (2025.emnlp-main)

Challenge:	Existing approaches to training or evaluating non-English dialogue datasets often introduce artifacts that reduce their naturalness and cultural appropriateness.
Approach:	They propose a structured framework for encoding, localizing, and generating multilingual dialogues from abstract intent representations.
Outcome:	The proposed framework outperforms translation models in Italian, German, and Chinese on cultural relevance, coherence, and situational appropriateness.

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Minki Hong, Jangho Choi, Jihie Kim

HCFD: A Benchmark for Audio Deepfake Detection in Healthcare (2026.findings-acl)

Challenge:	Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce coherent and socially acceptable outputs.
Approach:	They propose a framework for generating and annotating socially grounded dialogues in Chinese, English, and Korean.
Outcome:	The proposed framework outperforms existing frameworks in refinement quality, dialogue naturalness, and generalization performance.

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Mohd Mujtaba Akhtar, null Girish, Muskaan Singh

Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)

Challenge:	a new task for detecting codec-fakes under pathological speech conditions is presented . we focus on codec based synthetic speech since neural codec decoding is a core building block in speech generation pipelines.
Approach:	They propose a new task for detecting codec-fakes under pathological speech conditions . they focus on codec based synthetic speech since neural codec decoding is a core building block in speech pipelines .
Outcome:	The proposed framework outperforms speech-based models on Healthcare CodecFake . it achieves the strongest performance on the task across clinical conditions and codecs .

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Arturo Oncevay, Elena Kochkina, Keshav Ramani, Toyin Aguda, Simerjot Kaur, Charese Smiley

Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites (2025.emnlp-main)

Challenge:	Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings.
Approach:	They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole.
Outcome:	The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole.

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Xintong Wang, Yixiao Liu, Jingheng Pan, Liang Ding, Longyue Wang, Chris Biemann

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)

Challenge:	Large language models (LLMs) can be effective at rewriting toxic content, but they often default to overly polite rewrites, distorting the emotional tone and communicative intent.
Approach:	They evaluate 17 large language models with variant architectures to evaluate their ability to rewrite toxic content while preserving the speaker's original intent.
Outcome:	The first Chinese detoxification dataset explicitly designed to preserve sentiment polarity is evaluated across five real-world scenarios.

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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong

From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQA (2026.findings-acl)

Challenge:	Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages.
Approach:	They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English.
Outcome:	The proposed model outperforms open-source and Tibetan-focused models on diverse tasks.

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Jing Zhang, Lianghong Guo, Yanlin Wang, Terry Yue Zhuo, Yong Wang, Mingwei Liu, Jiachi Chen, Ensheng Shi, Yuchi Ma, Hongyu Zhang, Zibin Zheng

A Linguistics-Aware LLM Watermarking via Syntactic Predictability (2026.acl-long)

Challenge:	Existing Dev Knowledge QA benchmarks are limited in development knowledge scope and often not built from real user queries.
Approach:	They conduct preliminary analysis of real user–LLM dialogues from WildChat to investigate the importance of Dev Knowledge QA in AI-assisted software development scenarios.
Outcome:	The proposed benchmark is based on real user–LLM dialogues from WildChat.

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Shinwoo Park, Hyejin Park, Hyeseon An, Yo-Sub Han

Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages (2026.findings-acl)

Challenge:	a central challenge remains balancing text quality against detection robustness.
Approach:	They propose a framework that aligns watermark strength with linguistic degrees of freedom . they use part-of-speech models to weaken the signal in grammatically constrained contexts .
Outcome:	The proposed framework outperforms existing methods in linguistic indeterminacy tests on languages . it weakens the watermark strength in grammatically constrained contexts and strengthens it in contexts with greater linguistic flexibility.

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null Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Arun Balaji Buduru