Papers by Kai Yu

AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)

Challenge:	End-to-end speech-to speech (S2S) dialogue systems face key challenges in incorporating external knowledge into their models.
Approach:	They propose a framework that directly retrieves relevant textual knowledge from speech queries.
Outcome:	The proposed framework improves the performance of end-to-end speech-tospeech dialogue systems while achieving higher retrieval efficiency.

Copied to clipboard

Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li

META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)

Challenge:	Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks.
Approach:	They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness.
Outcome:	The proposed method improves LLMs’ safety over all baselines.

Copied to clipboard

Liangtai Sun, Xingyu Chen, Lu Chen, Tianle Dai, Zichen Zhu, Kai Yu

Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)

Challenge:	Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task.
Approach:	They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs.
Outcome:	The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs.

Copied to clipboard

Xiang Fang, Daizong Liu, Wanlong Fang, Pan Zhou, Yu Cheng, Keke Tang, Kai Zou

CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)

Challenge:	Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work .
Approach:	They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training.
Outcome:	The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets.

Copied to clipboard

Hanchong Zhang, Ruisheng Cao, Hongshen Xu, Lu Chen, Kai Yu

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)

Challenge:	Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks.
Approach:	They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain.
Outcome:	The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs.

Copied to clipboard

Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, Kai Yu

Multilingual Brain Surgeon: Large Language Models Can Be Compressed Leaving No Language behind (2024.lrec-main)

Challenge:	Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances.
Approach:	They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances.
Outcome:	The proposed framework is effective and compatible with supervised training.

Copied to clipboard

Hongchuan Zeng, Hongshen Xu, Lu Chen, Kai Yu

F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching (2025.acl-long)

Challenge:	Existing methods for MC focus on quantization and network pruning.
Approach:	They propose a calibration method that samples calibration data from various languages proportionally to the language distribution of the model training datasets.
Outcome:	The proposed method improves the performance of existing English-centric compression methods on the BLOOM multilingual LLM.

Copied to clipboard

Yushen Chen, Zhikang Niu, Ziyang Ma, Keqi Deng, Chunhui Wang, JianZhao JianZhao, Kai Yu, Xie Chen

Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)

Challenge:	Recent research in Text-to-Speech (TTS) has experienced great advancement . current models can synthesize speech for any given text and mimic the speaker of audio prompt.
Approach:	They propose a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT) without complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then denoising is performed for speech generation.
Outcome:	The proposed system achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based models.

Copied to clipboard

Jieyu Li, Lu Chen, Ruisheng Cao, Su Zhu, Hongshen Xu, Zhi Chen, Hanchong Zhang, Kai Yu

When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models (2025.emnlp-main)

Challenge:	Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated.
Approach:	They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
Outcome:	The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.

Copied to clipboard

Yingming Zheng, Hanqi Li, Kai Yu, Lu Chen

CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset (2023.findings-acl)

Challenge:	Large language models (LLMs) have achieved impressive performance across NLP tasks.
Approach:	They propose to use long-context SFT to improve short-contemporary performance . they also decouple and analyze two key components, Multi-Head Attention and Feed-Forward Network .
Outcome:	The proposed model improves short-context performance, contrary to pretraining.

Copied to clipboard

Hanchong Zhang, Jieyu Li, Lu Chen, Ruisheng Cao, Yunyan Zhang, Yu Huang, Yefeng Zheng, Kai Yu

A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)

Challenge:	a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain .
Approach:	They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases.
Outcome:	The proposed system can parse user questions into SQL on complete unseen databases.

Copied to clipboard

Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, Xin Song

Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark (2026.acl-long)

Challenge:	Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts.
Approach:	They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data.
Outcome:	The proposed model outperforms SOTA methods on the link prediction task.

Copied to clipboard

Zihan Zhang, Yu Bao, Xiao Ding, Tianyi Jiang, Kai Xiong

Challenge:	Existing benchmarks for EEG2Text have neglected EEG instability, a problem that has confounded inference and sparked debate.
Approach:	They propose to use a 128-channel high-density EEG cap to evaluate EEG2Text models . they find existing benchmarks have neglected EEG instability, a flaw that has confounded inferences and sparked debate .
Outcome:	The proposed benchmarks provide key evidence for teacher-forcing-free decoding of EEG2Text models.

LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations (2021.acl-long)

Copied to clipboard

Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, Kai Yu

Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)

Challenge:	Existing methods to encode text-to-SQL data are node-centric and ignore semantics embedded in the topological structure of edges.
Approach:	They propose a Line Graph Enhanced Text-to-SQL model to mine relational features without constructing meta-paths.
Outcome:	The proposed model achieves state-of-the-art on the cross-domain text-to-SQL benchmark Spider at the time of writing.

Copied to clipboard

Ke Wang, Jiayi Wang, Niyu Ge, Yangbin Shi, Yu Zhao, Kai Fan

Machine Translation

Challenge:	Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora.
Approach:	They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output.
Outcome:	The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model.

Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)

Copied to clipboard

Yu Yang, Jinyu Guo, Kai Shuang, Chenrui Mao

Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)

Challenge:	Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem .
Approach:	They propose an EAE model with stage-customized entity type embedding to explore the role of entity types.
Outcome:	The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings.

Copied to clipboard

Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Low-Resource NLP

Challenge:	Existing methods to generate valid and fluent questions from text are limited and insufficient for training.
Approach:	They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text.
Outcome:	The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements.

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)

Copied to clipboard

Zihan Zhao, Lu Chen, Ruisheng Cao, Hongshen Xu, Xingyu Chen, Kai Yu

Machine Reading Comprehension

Challenge:	Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases.
Approach:	They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process.
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing.

M-SENA: An Integrated Platform for Multimodal Sentiment Analysis (2022.acl-demo)

Copied to clipboard

Huisheng Mao, Ziqi Yuan, Hua Xu, Wenmeng Yu, Yihe Liu, Kai Gao

Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)

Challenge:	M-SENA is an open-source platform for multimodal sentiment analysis.
Approach:	They propose to use a platform for multimodal sentiment analysis to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations.
Outcome:	The proposed framework provides reliable benchmarks and baseline results of different modality features and MSA benchmarks.

Copied to clipboard

Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)

Challenge:	Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Approach:	They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons.
Outcome:	The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process.

Copied to clipboard

Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu

Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)

Challenge:	Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels .
Approach:	They propose a new architecture which processes schemas at abstract and semantic levels.
Outcome:	The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema .

Copied to clipboard

Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, Jiliang Tang

TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)

Challenge:	Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning.
Approach:	They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning.
Outcome:	Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models.

Copied to clipboard

Yiming Ai, Zhiwei He, Kai Yu, Rui Wang

Machine Translation

Challenge:	tense inconsistency is a common problem in machine translation systems.
Approach:	They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy.
Outcome:	The proposed model can measure the tense consistency performance of machine translation systems for the first time.

Dialogue-Based Relation Extraction (2020.acl-main)

Copied to clipboard

Dian Yu, Kai Sun, Claire Cardie, Dong Yu

Information Extraction & Text Mining

Challenge:	Existing dialogue-based relation extraction tasks focus on texts from formal genres such as professionally written and edited news reports or well-edited websites.
Approach:	They propose to use DialogRE to study cross-sentence relation extraction . they propose to annotate 36 possible relation types between arguments in dialogues .
Outcome:	The proposed dataset supports the prediction of relation(s) between two arguments that appear in a dialogue.

Taxonomy-Guided Zero-Shot Recommendations with LLMs (2025.coling-main)

Copied to clipboard

Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu

SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)

Challenge:	Existing approaches to deploy large language models (LLMs) into RecSys have limited prompt length, unstructured item information, and un-constrained generation of recommendations.
Approach:	They propose a taxonomy-guided recommendation framework that empowers LLMs with category information in a systematic approach.
Outcome:	The proposed framework significantly improves recommendation quality compared to zero-shot approaches.

Copied to clipboard

Sheng Jiang, Su Zhu, Ruisheng Cao, Qingliang Miao, Kai Yu

Semantic Parsing

Challenge:	Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent.
Approach:	They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent .
Outcome:	The proposed method improves on three multi-intent datasets on multi-tasks.

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

Copied to clipboard

Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

Copied to clipboard

Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang

Fixing Distribution Shifts of LLM Self-Critique via On-Policy Self-Play Training (2025.acl-long)

Challenge:	Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality.
Approach:	They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data.
Outcome:	The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data.

Copied to clipboard

Rong Bao, Donglei Yu, Kai Fan, Minpeng Liao

SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)

Challenge:	Large language models show impressive performance in a wide range of linguistic tasks, but their performance on complex reasoning tasks is still signif-icantly lower than the human level.
Approach:	They propose a reinforcement learning framework to synchronize the reasoning and critique capabilities of language models by using Monte Carlo sampling to give appropriate rewards to the model's critique content.
Outcome:	The proposed framework improves the model's reasoning and critique capabilities by 5.40 and 3.66 points, respectively, compared to the best baseline approach.

Copied to clipboard

Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen

Speech & Audio in NLP

Challenge:	a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens .
Approach:	They propose a timbre-controllable, end-to-end voice interaction system with single-stage training.
Outcome:	The proposed system outperforms previous models on 4 GPUs with limited data.

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)

Copied to clipboard

Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev

Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)

Challenge:	Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries.
Approach:	They propose a syntax tree network to address the complex text-to-SQL generation task.
Outcome:	The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus.

Copied to clipboard

Yanbin Zhao, Lu Chen, Zhi Chen, Ruisheng Cao, Su Zhu, Kai Yu

AdapterShare: Task Correlation Modeling with Adapter Differentiation (2022.emnlp-main)

Challenge:	Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data.
Approach:	They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs.
Outcome:	The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language.

Copied to clipboard

Zhi Chen, Bei Chen, Lu Chen, Kai Yu, Jian-Guang Lou

Green & Sustainable NLP

Challenge:	AdapterShare is an adapter differentiation method to explicitly model the task correlation among multiple tasks.
Approach:	They propose an adapter differentiation method to explicitly model the task correlation among multiple tasks.
Outcome:	The proposed method achieves 1.90 points improvement on five dialogue understanding tasks and 2.33 points gain on NLU tasks.

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Copied to clipboard

Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Data Augmentation with Atomic Templates for Spoken Language Understanding (D19-1)

Copied to clipboard

Zijian Zhao, Su Zhu, Kai Yu

Low-Resource NLP

Challenge:	Existing methods to enlarge SLU data require large amounts of labelled data.
Approach:	They propose a data augmentation method with atomic templates for Spoken Language Understanding which generates atomic exemplars from atomic template.
Outcome:	The proposed method improves on a DSTC 2&3 dataset which is a domain adaptation setting of SLU.

CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)

Copied to clipboard

Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu

ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought (2023.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding.
Approach:	They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning .
Outcome:	The proposed model can encode words into fine-grained representations without modification of production pipelines.

Copied to clipboard

Hanchong Zhang, Ruisheng Cao, Lu Chen, Hongshen Xu, Kai Yu

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)

Challenge:	Recent studies have focused on the development of semantic parsers within the framework of cross-domain analysis.
Approach:	They propose a method to generate auto-CoT exemplars using ACT-SQL and extend it to multi-turn text-to-Sql tasks.
Outcome:	The proposed method achieves SOTA performance on the Spider dev set among existing in-context learning approaches.

Copied to clipboard

Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu

Machine Translation

Challenge:	Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages .
Approach:	They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes .
Outcome:	The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step.

RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)

Copied to clipboard

Yuze Zhao, Zhenya Huang, Yixiao Ma, Rui Li, Kai Zhang, Hao Jiang, Qi Liu, Linbo Zhu, Yu Su

Semantic Parsing with Dual Learning (P19-1)

Challenge:	Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts.
Approach:	They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases.
Outcome:	The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions.

Copied to clipboard

Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu

Binarized LSTM Language Model (N18-1)

Challenge:	Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding.
Approach:	They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game.
Outcome:	The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset.

Copied to clipboard

Xuan Liu, Di Cao, Kai Yu

Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings (2021.findings-emnlp)

Challenge:	Long short-term memory (LSTM) language models are widely used for automatic speech recognition and natural language processing (NLP) however, they are limited by the word embedding layer.
Approach:	They propose to encode words into binary vectors and use binarized LSTM parameters to achieve high memory compression.
Outcome:	The proposed model achieves 11.3 compression ratio without loss of performance and 31.6 compression ratio with acceptable performance degradation.

Copied to clipboard

Kai Wang, Yu Liu, Dan Lin, Michael Sheng

Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)

Challenge:	Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations.
Approach:	They propose to use hyperbolic geometry to generate high-fidelity and parsimonious representations of hierarchical patterns in knowledge graphs.
Outcome:	The proposed models achieve state-of-the-art performance on two widely-used datasets and cost less than RotH.

Copied to clipboard

Su Zhu, Jieyu Li, Lu Chen, Kai Yu

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)

Challenge:	Existing methods to track dialogue state are limited due to data sparsity and long dialogues.
Approach:	They propose to use the previous dialogue state and current dialogue utterance as input for DST.
Outcome:	The proposed approach outperforms existing methods and improves existing ones.

Copied to clipboard

Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)

Challenge:	Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood.
Approach:	They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction .
Outcome:	The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones .

Copied to clipboard

Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu

Natural Language Interfaces

Challenge:	Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents.
Approach:	They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process.
Outcome:	The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets .

OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)

Copied to clipboard

Zhi Chen, Yuncong Liu, Lu Chen, Su Zhu, Mengyue Wu, Kai Yu

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

Challenge:	Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue.
Approach:	They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks.
Outcome:	The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.

Copied to clipboard

Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction (2026.findings-eacl)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

Copied to clipboard

Haoran Wang, Jiatong Shi, Jinchuan Tian, Bohan Li, Kai Yu, Shinji Watanabe

Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)

Challenge:	Neural audio codecs have enabled high-fidelity reconstruction of speech, music and sound . however, speech-optimized codec systems suffer degradation on music or sound if they ignore spectral differences .
Approach:	They propose a neural audio codec that splits the spectral dimension into separate bands and compresses each band independently.
Outcome:	Experimental results show that BSCodec achieves better reconstruction quality on music and sound compared to existing codecs.

Copied to clipboard

Lu Chen, Yanbin Zhao, Boer Lyu, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu

Structured Dialogue Policy with Graph Neural Networks (C18-1)

Challenge:	Chinese word segmentation can be erroneous, ambiguous or inconsistent, causing performance problems.
Approach:	They propose a sentence matching framework that uses paired word lattices as input instead of a character sequence.
Outcome:	The proposed framework outperforms the state-of-the-art short text matching models on two Chinese datasets.

Copied to clipboard

Lu Chen, Bowen Tan, Sishan Long, Kai Yu

Natural Language Interfaces

Challenge:	Recent advances focus on improving DRL-based dialogue policy optimization.
Approach:	They propose to design a graph neural network structure that is better suited for dialogue management.
Outcome:	The proposed approach outperforms state-of-the-art approaches in 18 tasks of the PyDial benchmark.

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)

Copied to clipboard

Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)

Challenge:	Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information.
Approach:	They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark.
Outcome:	The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%.

Copied to clipboard

Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev

Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)

Challenge:	Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models.
Approach:	They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries .
Outcome:	The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting.

Copied to clipboard

Zhi Chen, Lu Chen, Hanqi Li, Ruisheng Cao, Da Ma, Mengyue Wu, Kai Yu

Unsupervised Context Rewriting for Open Domain Conversation (D19-1)

Challenge:	Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems .
Approach:	They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed.
Outcome:	The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data.

Copied to clipboard

Kun Zhou, Kai Zhang, Yu Wu, Shujie Liu, Jingsong Yu

Text Generation

Challenge:	Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency.
Approach:	They propose a context rewriting method which explicitly rewrites the last utterance by considering context history.
Outcome:	The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots.

Improving Question Answering with External Knowledge (D19-58)

Copied to clipboard

Xiaoman Pan, Kai Sun, Dian Yu, Jianshu Chen, Heng Ji, Claire Cardie, Dong Yu

Question Answering

Challenge:	ARC-Easy, ARC Challenge, and OpenBookQA use Wikipedia to augment training data . performance degrades when additional instances exhibit higher difficulty than original training data.
Approach:	They propose two methods for exploiting external knowledge for QA in science . they enrich the original corpus with relevant text snippets from an open-domain resource . the second method simply increases the amount of training data by appending additional in-domain instances.
Outcome:	The proposed methods achieve gains in accuracy of 8.1%, 13.0%, and 12.8% on science QA tasks.

Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models (2026.acl-long)

Copied to clipboard

Jiahuan Zhang, Shunwen Bai, Tianheng Wang, KaiWen Guo, Zijia Song, Hanqing WU, Guozheng Rao, Kai Han, Kaicheng Yu

RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)

Challenge:	Existing benchmarks explore aspects of threedimensional spatial reasoning and visual-language reasoning in dynamic environments, but they are unable to perform well on 3D spatial deformation reasoning.
Approach:	They propose to use a ladder competition format to assess the model's spatial deformation reasoning abilities to determine its performance.
Outcome:	The proposed framework assesses the performance of Vision-Language Models in spatial deformation reasoning tasks.

Copied to clipboard

Pinyi Zhang, Siyu An, Lingfeng Qiao, Yifei Yu, Jingyang Chen, Jie Wang, Di Yin, Xing Sun, Kai Zhang

Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021.findings-emnlp)

Challenge:	Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction.
Approach:	They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players.
Outcome:	The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels.

Copied to clipboard

Dian Yu, Kai Sun, Dong Yu, Claire Cardie

Question Answering

Challenge:	despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models.
Approach:	They propose to use QA data to generate more training data for machine reading comprehension tasks by crowdsourcing . they first collect a large-scale multiple-choice QA dataset for Chinese, ExamQA, and then use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance.
Outcome:	The proposed model improves a Chinese MRC task with +5.1% accuracy and +3.8% exact match.

Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark (2026.acl-long)

Copied to clipboard

Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, Ziwei Liu

ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)

Challenge:	Existing evaluations treat visual understanding and generation in isolation or overlook tasks that inherently couple them.
Approach:	They propose a benchmark that examines the bidirectional synergy between generation and understanding across eight reasoning-centric domains.
Outcome:	The proposed model systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains.

Copied to clipboard

Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi

Challenge:	Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential.
Approach:	They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling.
Outcome:	The proposed model improves performance on four benchmarks and significantly mitigates positional bias.

From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)

Copied to clipboard

Yang Han, Ziping Wan, Lu Chen, Kai Yu, Xin Chen

Towards Universal Dialogue State Tracking (D18-1)

Challenge:	Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry.
Approach:	They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools.
Outcome:	The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research.

Copied to clipboard

Liliang Ren, Kaige Xie, Lu Chen, Kai Yu

Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)

Challenge:	Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains.
Approach:	They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots.
Outcome:	The proposed system significantly outperforms state-of-the-art approaches on two datasets.

Copied to clipboard

Yu Yuan, Lili Zhao, Kai Zhang, Guangting Zheng, Qi Liu

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction.
Approach:	They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance.
Outcome:	The proposed test suite incorporates six shortcut types, five evaluation metrics, and four prompting strategies.

Copied to clipboard

Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

Copied to clipboard

Zewen Long, Yu Peng, Fangming Dong, Congyi Li, Xingmao Guan, Shu Wu, Kai Chen

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)

Challenge:	Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate .
Approach:	They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization.
Outcome:	The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants.

Copied to clipboard

Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo

Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)

Challenge:	a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers .
Approach:	They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context.
Outcome:	The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters.

Copied to clipboard

Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, Huan Sun

WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021.emnlp-main)

Challenge:	Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem.
Approach:	They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches .
Outcome:	The proposed methods highlight promising signals and challenges.

Copied to clipboard

Xingyu Chen, Zihan Zhao, Lu Chen, JiaBao Ji, Danyang Zhang, Ao Luo, Yuxuan Xiong, Kai Yu

ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary (2025.coling-main)

Challenge:	Using a web page and a question, a machine can't understand the contents of web pages.
Approach:	They propose a novel dataset for web-based structural reading comprehension that consists of 400K question-answer pairs and a dataset of 6.4K web pages.
Outcome:	The proposed dataset consists of 400K question-answer pairs, collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata.

Copied to clipboard

Yutong Li, Lu Chen, Aiwei Liu, Kai Yu, Lijie Wen

Text Generation

Challenge:	Literature review is an indispensable step in the research process, but literature summary is challenging and time consuming.
Approach:	They propose an LLM agent with human workflow guidance for comparative literature summary . they use a human workflow to extract key elements from relevant literature and generate summaries .
Outcome:	The proposed method outperforms the CoT model in several dimensions.

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)

Copied to clipboard

Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)

Challenge:	Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage.
Approach:	They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE.
Outcome:	The proposed framework can be used to infer whether a specific relation exists in a private client.

Copied to clipboard

Shijia Xu, Yu Wang, Xiaolong Jia, Zhou Wu, Kai Liu, April Xiaowen Dong

Challenge:	despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions.
Approach:	They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation.
Outcome:	The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency.

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Copied to clipboard

Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

Copied to clipboard

Binwei Yao, Chao Shi, Likai Zou, Lingfeng Dai, Mengyue Wu, Lu Chen, Zhen Wang, Kai Yu

Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models (2025.coling-main)

Challenge:	Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness.
Approach:	They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria.
Outcome:	The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset.

Copied to clipboard

Hongchuan Zeng, Senyu Han, Lu Chen, Kai Yu

IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation (2024.acl-long)

Challenge:	Recent studies suggest that large language models can transfer skills learned in one language to others, but internal mechanisms behind this ability remain unclear.
Approach:	They find that LLMs map semantically identical inputs from different languages into a common semantic latent space that allows for consistent processing across languages.
Outcome:	The findings highlight the structural evolution of multilingual models during training and scaling up.

Copied to clipboard

Senyu Han, Lu Chen, Li-Min Lin, Zhengshan Xu, Kai Yu

Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction (2021.findings-emnlp)

Challenge:	Language models have demonstrated their capabilities in storyline creation and human-like character role-playing.
Approach:	They propose a director-actor coordinate agent framework that generates drama scripts . framework allows actors to role-play their characters while maintaining plot development .
Outcome:	The proposed framework generates drama scripts from a drama plot outline and human actors can play their characters.

Copied to clipboard

Boer Lyu, Lu Chen, Kai Yu

Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

Challenge:	Sememes are defined as the atomic units to describe the semantic meaning of concepts.
Approach:	They propose a method which incorporates internal Chinese character information to help sememe prediction.
Outcome:	The proposed method outperforms existing non-external information models on howNet, a famous sememe knowledge base.

Copied to clipboard

Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu, April Xiaowen Dong

Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks (2024.lrec-main)

Challenge:	Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach:	They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome:	The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

Copied to clipboard

Ruiyang Zhou, Lu Chen, Kai Yu

Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)

Challenge:	Existing datasets and methods targeting review-related tasks have not thoroughly inspected model's review ability.
Approach:	They propose to evaluate GPT-3.5 and GPT-4 on two types of tasks under different settings: the score prediction task and the review generation task.
Outcome:	The proposed model can give passable decisions (> 60% accuracy) on single options, but it always makes mistakes.

Copied to clipboard

Kai Wang, Junfeng Tian, Rui Wang, Xiaojun Quan, Jianxing Yu

URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Models (2025.findings-emnlp)

Challenge:	Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation.
Approach:	They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts.
Outcome:	The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset.

Copied to clipboard

Ruiqi Yan, Xiquan Li, Wenxi Chen, Zhikang Niu, Chen Yang, Ziyang Ma, Kai Yu, Xie Chen

Speech & Audio in NLP

Challenge:	a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios is a major challenge for end-to end spoken dialogue models.
Approach:	They propose to provide an extensive evaluation framework for end-to-end spoken dialogue models (SDMs) that includes both cognitive dimensions and paralinguistic cues .
Outcome:	The proposed benchmark is divided into two difficulty levels: basic track and pro track, each comprising 20 test sets, evaluating the spoken dialogue model’s abilities in Understanding, Reasoning, and O**ral conversation.

CritiQ: Mining Data Quality Criteria from Human Preferences (2025.acl-long)

Copied to clipboard

Honglin Guo, Kai Lv, Qipeng Guo, Tianyi Liang, Zhiheng Xi, Demin Song, Qiuyinzhe Zhang, Yu Sun, Kai Chen, Xipeng Qiu, Tao Gui

Information Retrieval

Challenge:	Existing methods to train language models rely on manual design, perplexity, or careful prompt engineering.
Approach:	They propose a method that automatically mines criteria from human preferences for data quality with only 30 human-annotated pairs and performs efficient data selection.
Outcome:	The proposed method improves on human-annotated test sets and shows high accuracy on code, math, and logic domains.

Mixture of insighTful Experts (MoTE): The Synergy of Reasoning Chains and Expert Mixtures in Self-Alignment (2025.acl-long)

Copied to clipboard

Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James Kwok

Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)

Challenge:	Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment.
Approach:	They propose a framework that synergistically combines reasoning chains and expert mixtures to improve self-alignment.
Outcome:	The proposed framework improves model safety, jailbreak resistance, and over-refusal capabilities, achieving performance comparable to OpenAI’s state-of-the-art o1 model.

Copied to clipboard

Kai Zhang, Bernal Jimenez Gutierrez, Yu Su

AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)

Challenge:	Recent work has shown that fine-tuning large language models on large instruction-following datasets improves their performance on a wide range of NLP tasks, but they fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task.
Approach:	They propose a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets.
Outcome:	The proposed framework outperforms small LLMs on relation extraction (RE), a fundamental information extraction task, by a large margin.

Copied to clipboard

Yang Han, Yiming Wang, Rui Wang, Lu Chen, Kai Yu

Information Extraction & Text Mining

Challenge:	Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets.
Approach:	They propose a human summarization preference alignment framework to align PLMs with human preferences.
Outcome:	The proposed framework narrows the gap between automatic and human evaluations by integrating three components.

Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)

Copied to clipboard

Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang

Multilinguality

Challenge:	Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding.
Approach:	They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data .
Outcome:	The proposed method improves translation quality without hurting unconstrained words.

Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models (2024.emnlp-main)

Copied to clipboard

Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Ong, Beong-woo Kwak, Moohyeon Kim, Sunghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo

Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)

Challenge:	Prior work has used LLMs to generate programming language and applied external compilers for such tasks.
Approach:	They propose a framework that expresses task-level logic with pseudocode and tailors it to each instance and simulates execution of it.
Outcome:	The proposed framework outperforms baselines in diverse reasoning tasks.

Copied to clipboard

Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Claire Cardie

Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (2020.tacl-1)

Challenge:	Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well.
Approach:	They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data.
Outcome:	The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3.

Copied to clipboard

Kai Sun, Dian Yu, Dong Yu, Claire Cardie

Improving Machine Reading Comprehension with General Reading Strategies (N19-1)

Challenge:	''Language is, at best, a means of directing others to construct similar-thoughts from their own prior knowledge,'' says K. S. Adams and Bruce.
Approach:	They present a free-form multiple-choice Chinese machine reading Comprehension dataset (C3) containing 13,369 documents and their associated 19,577 multiple-CHOice free- form questions.
Outcome:	The proposed model outperforms human models on linguistic, domain-specific, and general world knowledge problems.

Copied to clipboard

Kai Sun, Dian Yu, Dong Yu, Claire Cardie

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)

Challenge:	Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge.
Approach:	They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task.
Outcome:	The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE.

Copied to clipboard

Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan