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Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation (2024.eacl-long)

Challenge:	Recent advances in large language model pruning have shown high predictive performance in post-training settings.
Approach:	They conduct an empirical study on the performance and internal representation changes associated with pruning multilingual models for monolingual applications.
Outcome:	The proposed pruning methods retain perplexity and yield high signal-to-noise ratios, but not consistently improve downstream tasks.

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Shira Wein, Nathan Schneider

Text Generation

Challenge:	Abstract Meaning Representation (AMR) can be used to reduce translationese in text . if translationeses are not addressed in training or test sets, evaluation scores can be overinflated .
Approach:	They propose to use Abstract Meaning Representation (AMR) to reduce translationese in translated texts.
Outcome:	The proposed approach outperforms other methods based on machine translation and paraphrase generation.

Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation (2021.naacl-main)

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Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad

Responsible & Trustworthy NLP

Challenge:	Recent advances in summarization are driven by the availability of large datasets such as the CNN-DailyMail corpus and the New York Times corpus.
Approach:	They propose a method for fine-tuning pretrained models for summarization in unsupervised manner . they use Wikipedia data to produce pseudo-summaries which contain characteristics of target dataset .
Outcome:	The proposed method achieves state-of-the-art, zero-shot abstractive summarization performance on CNN-DailyMail dataset and compares with other methods on other datasets.

A Multilingual Wikified Data Set of Educational Material (L18-1)

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Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal van den Bosch

Spelling convention sensitivity in neural language models (2023.findings-eacl)

Challenge:	a crowdsourcing effort to annotate and link parallel texts has been unsuccessful . a data set of parallel texts in eleven languages is presented .
Approach:	They present a wikified data set of English sentences linked to Wikipedia pages . they use crowdsourcing to annotate the texts and perform crowdsourcing for complex annotations .
Outcome:	The proposed data set is valuable as it constitutes a rich resource . it includes annotated data of English sentences linked to translations in eleven languages .

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Elizabeth Nielsen, Christo Kirov, Brian Roark

Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding (2022.naacl-main)

Challenge:	Various long-distance dependencies have been investigated using neural language models.
Approach:	They examine whether large neural language models learn the long-distance dependency of British versus American spelling conventions . a large T5 language model does internalize consistency, but only with respect to observed lexical items .
Outcome:	The proposed model internalizes consistency with the training corpora, but only with respect to observed lexical items.

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Ao Jia, Yu He, Yazhou Zhang, Sagar Uprety, Dawei Song, Christina Lioma

Multimodality

Challenge:	Desire is a primitive instinct and a need for strongly expressing human desires to get or possess something.
Approach:	They propose to use MSED to model and understand human desire . they propose to provide a benchmark for human desire analysis .
Outcome:	The proposed dataset contains 9,190 text-image pairs with English text.

Domain Regeneration: How well do LLMs match syntactic properties of text domains? (2025.findings-acl)

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Da Ju, Hagen Blix, Adina Williams

Syntactic Text Processing

Challenge:	Recent improvements in large language models have improved their ability to approximate distributions . authors find that LLMs can suffer from model collapse due to domain considerations based on pretraining .
Approach:	They use open source LLMs to regenerate permissively licensed English text from Wikipedia and news text.
Outcome:	The proposed model can faithfully match the human-generated distributions in a semantically-controlled setting.

Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text (2026.findings-eacl)

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Piyush Singh Pasi

Multimodality

Challenge:	Existing multimodal models rely on machine translation, but performance drops for other languages due to limited multilingual multimodal resources.
Approach:	They propose a lightweight alignment method that learns only a few linear layers using English text alone to map multilingual text embeddings into multimodal space.
Outcome:	M2M achieves strong zero-shot transfer on XTD Text-to-Image retrieval in English and spanish . it learns only a few linear layers to map multilingual text embeddings into multimodal space .

GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres (2024.eacl-long)

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Jessica Lin, Amir Zeldes

MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)

Challenge:	Existing work on salient entity extraction relies on crowdsourcing or user statistics to derive labels for entities.
Approach:	They propose a dataset that defines salience using human summaries and shows high agreement between annotations based on whether a source entity is mentioned in the summary.
Outcome:	The proposed dataset shows that pre-trained models and zero-shot LLM prompting fail to capture salient entities in generated summaries.

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Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Nguyen

Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)

Challenge:	Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models.
Approach:	They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION .
Outcome:	a new dataset that consistently annotates events for 8 different languages is released . the new dataset will promote future research on multilingual ED .

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Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, Jian Wang

Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction (2022.acl-long)

Challenge:	Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities.
Approach:	They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese.
Outcome:	The proposed model can learn the interaction of bilinguals and dependent information within Chinese.

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Keshav Kolluru, Muqeeth Mohammed, Shubham Mittal, Soumen Chakrabarti, null Mausam

Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)

Challenge:	Existing training data in English is limited to supervised Open Information Extraction (OpenIE) .
Approach:	They propose a model that automatically converts English sentences into other languages by using Alignment-Augmented Constrained Translation (AACTrans) they train a generative OpenIE model that outputs for each sentence relations in the first stage and all extractions containing the relation in the second stage.
Outcome:	The proposed model outperforms existing models on Spanish, Portuguese, Chinese, Hindi and Telugu on 5 languages.

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Terra Blevins, Luke Zettlemoyer

MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)

Challenge:	a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models.
Approach:	They propose to use English pretraining corpora to analyze their language composition . they find that even when less than 1% of data is not English, it facilitates cross-lingual transfer .
Outcome:	The proposed model is not truly monolingual when pretrained at scale, the authors show . they show that even when less than 1% of data is not English, it facilitates cross-lingual transfer .

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Amir Pouran Ben Veyseh, Viet Lai, Chien Nguyen, Franck Dernoncourt, Thien Nguyen

CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)

Challenge:	Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English.
Approach:	They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages.
Outcome:	The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters .

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Weixin Zeng, Xiang Zhao, Jiuyang Tang, Zhen Tan, Xuqian Huang

Knowledge Representation

Challenge:	Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese .
Approach:	They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure .
Outcome:	The proposed corpus is based on 100 documents from diverse domains and is publicly accessible.

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

Robustness in NLP

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 .

WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models (2022.naacl-main)

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Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz

Semantic Text Processing

Challenge:	Existing methods to train large pretrained language models require more computational resources and are expensive to train in other languages.
Approach:	They propose a method to transfer pretrained language models to new languages using subword-based tokenization and embeddings.
Outcome:	The proposed method outperforms existing methods on low-resource languages and makes training large models more accessible and less damaging to the environment.

On the Multilingual Capabilities of Very Large-Scale English Language Models (2022.lrec-1)

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Jordi Armengol-Estapé, Ona de Gibert Bonet, Maite Melero

Semantic Text Processing

Challenge:	Generative Pre-trained Transformers (GPTs) have been scaled to unprecedented sizes in the history of machine learning.
Approach:	They investigate the potential and limits of Generative Pre-trained Transformers in three tasks . they find it can be almost as useful for many languages as it is for English .
Outcome:	The proposed model can perform tasks in five different languages, and its potential is explored . it can learn from a few examples "via text interaction" and is scalable to many languages .

Author Profiling from Facebook Corpora (L18-1)

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Fernando Hsieh, Rafael Dias, Ivandré Paraboni

Challenge:	Existing studies on author profiling focus on age and gender, and use only English text.
Approach:	They propose to model author profiling from a Brazilian Portuguese corpus using standard gender and age prediction tasks and two less-known alternatives: predicting an author's degree of religiosity and IT background status.
Outcome:	The proposed tasks are based on a Brazilian Portuguese corpus and are compared with other languages and tasks.

Toward Gender-Inclusive Coreference Resolution (2020.acl-main)

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Yang Trista Cao, Hal Daumé III

Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding (2020.emnlp-main)

Challenge:	a recent study shows that coreference resolution systems can be harmful to binary and non-binary trans and cis stakeholders.
Approach:	They propose to use gender-based crowd annotations to investigate coreference resolution biases . they use a dataset to examine the complexity of gender in crowd annotation systems .
Outcome:	a new study shows that without acknowledging and building systems that recognize gender, we build systems that lead to many potential harms.

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Samson Tan, Shafiq Joty, Lav Varshney, Min-Yen Kan

Responsible & Trustworthy NLP

Challenge:	Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English.
Approach:	They propose a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols.
Outcome:	The proposed method defends against inflectional adversaries while maintaining performance on clean data.

Question Translation Training for Better Multilingual Reasoning (2024.findings-acl)

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Wenhao Zhu, Shujian Huang, Fei Yuan, Shuaijie She, Jiajun Chen, Alexandra Birch

Encode, Tag, Realize: High-Precision Text Editing (D19-1)

Challenge:	Large language models have shown compelling performance on reasoning tasks but they tend to perform much worse in languages other than English.
Approach:	They propose to train a model to translate reasoning questions into English by fine tuning on X-English parallel question data.
Outcome:	The proposed approach improves on LLaMA2-13B on the MGSM and MSVAMP multilingual reasoning benchmarks.

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Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, Aliaksei Severyn

Syntactic Text Processing

Challenge:	Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts.
Approach:	They propose a sequence tagging approach that casts text generation as a text editing task.
Outcome:	The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance.

Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR (2024.lrec-main)

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Kenneth Lai, Richard Brutti, Lucia Donatelli, James Pustejovsky

Multimodality

Challenge:	Abstract Meaning Representation (AMR) was designed to represent sentence meaning in English text, but recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture.
Approach:	They propose to annotate a multimodal (speech and gesture) AMR corpus in a task-based setting and capture coreference relationships across modalities.
Outcome:	The proposed corpus captures coreference relationships across modalities, enabling fine-grained analysis of how gesture and natural language interact.

MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation (2023.findings-acl)

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Sebastian Vincent, Robert Flynn, Carolina Scarton

Machine Translation

Challenge:	Existing research has focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker’s gender.
Approach:	They introduce a novel neural machine translation framework that interprets all context as text.
Outcome:	The proposed framework outperforms a baseline that matched the parameters and significantly outperformed it in English translation.

ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries (2021.emnlp-main)

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Autumn Toney, Aylin Caliskan

Representation Learning

Challenge:	Word embeddings learn implicit biases from word co-occurrence statistics . valNorm is a new intrinsic evaluation task and method to quantify affect in word embedded word sets .
Approach:	They propose a method to quantify valence dimension of affect in human-rated word sets . they apply ValNorm to embeddings from seven languages and 200 years of text .
Outcome:	The proposed method achieves a high accuracy in quantifying the valence of non-discriminatory, non-social group word sets.

Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies (2022.acl-long)

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Angela Fan, Claire Gardent

Ethical NLP

Challenge:	Existing efforts to encourage article creation focus on reducing the gender gap in Wikipedia articles.
Approach:	They propose a model that retrieves web evidence and generates biographies section by section . they analyze available web evidence to determine the accuracy of the generated text .
Outcome:	The proposed model can generate biographies section by section, including citation information, using retrieval mechanisms and a cache-based pre-trained encoder-decoder.

CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)

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Bhavani Shankar P S V N, Preethi Jyothi, Pushpak Bhattacharyya

Cross-lingual and Multilingual CLIP (2022.lrec-1)

Challenge:	More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored .
Approach:	They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules.
Outcome:	The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech.

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Fredrik Carlsson, Philipp Eisen, Faton Rekathati, Magnus Sahlgren

Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? (2024.findings-emnlp)

Challenge:	OpenAI released CLIP, a model that relates the textual and visual domains with unprecedented accuracy.
Approach:	They propose to use cross-lingual teacher learning to re-train an English textual encoder using a large dataset of images and captions.
Outcome:	The proposed method outperforms baselines on multilingual image-text retrieval while retaining low cost.

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Tannon Kew, Florian Schottmann, Rico Sennrich

Studying word order through iterative shuffling (2021.emnlp-main)

Challenge:	Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications.
Approach:	They investigate the minimal amount of multilinguality required during finetuning to elicit effective cross-lingual generalisation in English-centric LLMs.
Outcome:	The proposed model can respond in as few as two to three languages to a user's query in English, but the degree to which a target language is seen during pretraining is limiting.

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Nikolay Malkin, Sameera Lanka, Pranav Goel, Nebojsa Jojic

KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis (2025.acl-long)

Challenge:	Recent work on large language models has made this hypothesis popular . but, word order is not important enough to make sentence structure relevant .
Approach:	They propose an efficient procedure that finds word order having highest likelihood under a fixed language model.
Outcome:	The proposed procedure can be used to find the ordering of a bag of words having the highest likelihood under a fixed language model.

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Shinwoo Park, Shubin Kim, Do-Kyung Kim, Yo-Sub Han

Syntactic Text Processing

Challenge:	Detecting LLM-generated text is crucial for academic integrity, preventing plagiarism, protecting copyrights, ethical research practices.
Approach:	They propose a method specifically designed for Korean language to detect LLM-generated text . they examine spacing patterns, part-of-speech diversity, and comma usage .
Outcome:	The proposed method achieves an average of 19.78% higher AUC-ROC compared to the best-performing detection method.

Revisiting Entropy Rate Constancy in Text (2023.findings-emnlp)

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Vivek Verma, Nicholas Tomlin, Dan Klein

Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)

Challenge:	Existing evidence supports the uniform information density hypothesis . however, we re-evaluate the hypothesis with neural language models .
Approach:	They propose to use n-gram language models to argue that English documents exhibit entropy rate constancy . they re-evaluate the claims of Genzel and Charniak with neural language models .
Outcome:	The proposed hypothesis fails to support the proposed hypothesis with language models.

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Seoyeon Kim, Huiseo Kim, Chanjun Park, Jinyoung Yeo, Dongha Lee