Papers by Hai Zhao
Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)
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| Challenge: | Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge. |
| Approach: | They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before. |
| Outcome: | The proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of the proposed framework. |
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)
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| Challenge: | Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. |
| Approach: | They propose a bidirectional information decoupling network as a universal dialogue encoder which explicitly incorporates both the past and future contexts. |
| Outcome: | The proposed model incorporates past and future contexts and can be generalized to a wide range of dialogue-related tasks. |
Head-wise Shareable Attention for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. |
| Approach: | They propose two methods that share parameters across attention heads to reduce memory usage and reduce performance drop by using coarse-grained weight sharing rules. |
| Outcome: | The proposed methods reuse pre-trained weights without retraining and then share, denoted as PostShare. |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment (2024.findings-acl)
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| Challenge: | Existing methods of acceleration require fine-tuning of considerably large models, such as Llama-7B, posing a challenge for average users. |
| Approach: | They propose a Guidance-based Knowledge Transfer framework that leverages a larger LLM as a 'teacher' and a smaller 'student' model to finalize responses. |
| Outcome: | The proposed framework achieves a maximum accuracy improvement of 14.18%, along with a 10.72 times speed-up on GSM8K and an accuracy improvement 14.00% along with 7.73 times speed up in CSQA. |
Lattice-Based Transformer Encoder for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) takes deterministic sequences for source representations. However, word-level or subword-level segmentation has multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. |
| Approach: | They propose lattice-based encoders to explore effective word or subword representations in an automatic way during training. |
| Outcome: | The proposed encoders can explore effective word or subword representation in an automatic way during training. |
Towards Enhanced Immersion and Agency for LLM-based Interactive Drama (2025.acl-long)
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| Challenge: | Existing studies have focused on the role of immersion and agency in interactive drama. |
| Approach: | They propose a playwriting-guided generation method that helps LLMs craft dramatic stories with substantially improved structures and narrative quality. |
| Outcome: | The proposed method improves storytelling quality and immersion and agency, while allowing agents to refine their reactions to align with the player’s intentions. |
Query Rewriting in Retrieval-Augmented Large Language Models (2023.emnlp-main)
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| Challenge: | Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself. |
| Approach: | They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read . |
| Outcome: | The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA. |
Encoder and Decoder, Not One Less for Pre-trained Language Model Sponsored NMT (2023.findings-acl)
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| Challenge: | Existing methods for neural machine translation (NMT) use encoder-only enhancement or rely on specific multilingual PLMs. |
| Approach: | They propose a monolingual PLM-sponsored NMT model that lets both encoder and decoder enjoy PLM enhancement. |
| Outcome: | The proposed model improves on IWSLT14 En-De, De-En, WMT14 En -De and En-Fr tasks and achieves new state-of-the-art. |
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA (2024.naacl-long)
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| Challenge: | Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. |
| Approach: | They propose a framework to explicitly utilize the massive knowledge encoded in LLM parameters and their strong instruction understanding abilities. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on three widely-used ODQA datasets and achieves comparable performance with customized fine-tuned models on full training data. |
Structural Characterization for Dialogue Disentanglement (2022.acl-long)
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| Challenge: | tangled multi-party dialogues lead to difficulties in understanding the dialogue history for both human and machine. |
| Approach: | They propose a model for disentangling multi-party dialogues using speaker property and reference dependency. |
| Outcome: | The proposed model achieves state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension. |
Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)
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| Challenge: | Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries. |
| Approach: | They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time. |
| Outcome: | The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE. |
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)
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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim
| Challenge: | Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy. |
| Approach: | They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. |
| Outcome: | Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency. |
Dialogue Graph Modeling for Conversational Machine Reading (2021.findings-acl)
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| Challenge: | Existing methods for conversational machine reading (CMR) are not effective for capturing multiple objects in complex interactive scenarios. |
| Approach: | They propose a dialogue graph modeling framework that captures explicit and implicit interactions hidden in the rule documents and a model that asks clarification questions to the machine. |
| Outcome: | The proposed model exceeds the milestone accuracy score of 80% on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone precision score of 90%. |
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
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| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents (2025.acl-long)
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| Challenge: | Traditional Turing test limits each participant to one message at a time and requires constant human participation. |
| Approach: | They propose to enhance the original Turing test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
| Outcome: | The proposed test improves the original test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
Language-Codec: Bridging Discrete Codec Representations and Speech Language Models (2025.acl-long)
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Shengpeng Ji, Minghui Fang, Jialong Zuo, Ziyue Jiang, Dingdong Wang, Hanting Wang, Hai Huang, Zhou Zhao
| Challenge: | Existing gaps between discrete acoustic codecs and downstream speech language models . initial channel of codebooks contains excessive information, making it difficult to generate tokens from weakly supervised signals such as text. |
| Approach: | They propose a discrete acoustic codec for generating acustic tokens from weakly supervised signals. |
| Outcome: | The proposed language-codec outperforms competing audio compression algorithms and validates on downstream speech language models. |
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods of logical reasoning focus on entity-aware information but ignore hierarchical relations that may even have mutual effects. |
| Approach: | They propose a holistic graph network that deals with context at both discourse-level and word-level as the basis for logical reasoning. |
| Outcome: | The proposed method improves on logical reasoning QA datasets and natural language inference datasets. |
RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding (2026.findings-acl)
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| Challenge: | Existing methods for decoding large language models generate one token per step, causing high inference latency. |
| Approach: | They propose a method that integrates retrieved exact patterns with logit-driven future cues. |
| Outcome: | Experiments on Spec-Bench, HumanEval, and MGSM-ZH show that RACER outperforms training-free methods and accelerates inference. |
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning (2022.findings-emnlp)
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| Challenge: | Empirical studies show that learning multiple training objectives in a single model makes the learned language representation barely converge to the desired optimum. |
| Approach: | They propose a meta-learning-based adaptive sampler which learns latent sampling pattern on arbitrary pre-training objectives. |
| Outcome: | Empirical studies show that learning multiple objectives in a single model makes it difficult to achieve the desired optimum. |
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
| Approach: | They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters. |
| Outcome: | The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation. |
Can Language Models Serve as Temporal Knowledge Bases? (2022.findings-emnlp)
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| Challenge: | Existing studies have only considered language models as knowledge bases in a static setting . memorizing conflicting information is still challenging for LMs and hinders memorization of other unrelated one-to-one relationships. |
| Approach: | They propose two requirements for treating language models as temporal knowledge bases . they propose a dataset which is aimed at probing temporally-scoped knowledge . |
| Outcome: | The proposed model can store conflicting information and use stored knowledge for temporal knowledge queries. |
Instance Regularization for Discriminative Language Model Pre-training (2022.emnlp-main)
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| Challenge: | Existing studies have optimized independent strategies of ennoising or denosing . Existing methods treat training instances equally throughout the training process . |
| Approach: | They propose to use ennoising and denoising to train discriminative pre-trained language models . they propose to model the complexity of restoring the original sentences from corrupted ones . |
| Outcome: | Experimental results show that the proposed method improves pre-training efficiency, effectiveness, and robustness. |
Dialogue-oriented Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models (PrLMs) have shown impressive improvements for various downstream tasks including various dialogue related ones. |
| Approach: | They propose to use pre-trained language models to simulate dialogue features on general plain text with common language model training objectives to improve performance. |
| Outcome: | The proposed method is fine-tuned on three public multi-turn dialogue datasets and achieves significant and consistent improvement over the plain PrLMs. |
GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
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| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning (2022.coling-1)
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| Challenge: | Existing approaches to AMR focus on one-side improvements despite the duality of the two tasks . instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition. |
| Approach: | They propose a data-efficient bidirectional Bayesian learning approach to facilitate bidirectional information transition by adopting a single-stage multitasking strategy. |
| Outcome: | The proposed model outperforms existing models on benchmark datasets without extra training data. |
Smoothing Dialogue States for Open Conversational Machine Reading (2021.emnlp-main)
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| Challenge: | Existing studies train independent or pipeline systems for the two subtasks but are trivial by using hard-label decisions to activate question generation. |
| Approach: | They propose a method to smooth two dialogue states in one decoder and bridge decision making and question generation to provide a richer dialogue state reference. |
| Outcome: | The proposed method achieves state-of-the-art on the OR-ShARC dataset. |
Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed. |
| Approach: | They propose a framework to evaluate Chinese language teachers' pedagogical competence against international standards. |
| Outcome: | The proposed framework evaluates 13 latest multilingual and Chinese LLMs against international standards for Chinese language teachers. |
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Explicit Span-Sentence Predication solves location unit ambiguity problem in many languages, allowing model to determine which sentence contains the answer span when sentence itself has not been clearly defined at all. |
| Approach: | They propose a machine-learning reader with Explicit Span-Sentence Predication to solve this problem by analyzing Chinese sentences. |
| Outcome: | The proposed reader achieves state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages. |
Tracing Origins: Coreference-aware Machine Reading Comprehension (2022.acl-long)
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| Challenge: | a recent study has enriched pre-trained language models with syntactic, semantic and other linguistic information to improve their performance. |
| Approach: | They use a pre-trained language model to leverage coreference information to enhance word embeddings . they use additional encoder layers to focus on coreference mentions or a relational graph convolutional network to model the coreference relations. |
| Outcome: | The proposed model imitates the human reading process and leverages coreference information to enhance word embeddings. |
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
| Outcome: | The proposed model improves the state-of-the-art in GEC and improves on benchmarks. |
Composite Backdoor Attacks Against Large Language Models (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated superior performance on various tasks, but untrustworthy third-party LLMs may covertly introduce vulnerabilities for downstream tasks. |
| Approach: | They propose a composite backdoor attack that scatters multiple trigger keys in different prompt components. |
| Outcome: | The proposed attack achieves 100% Attack Success Rate (ASR) with a False Triggered Rate (FTR) below 2.06% and negligible model accuracy degradation. |
Named Entity Recognition Only from Word Embeddings (2020.emnlp-main)
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| Challenge: | Existing named entity recognition systems require large amounts of human annotated training data. |
| Approach: | They propose a fully unsupervised named entity recognition model which takes clues from pre-trained word embeddings. |
| Outcome: | The proposed model can be trained on two CoNLL benchmark datasets without annotating lexicon or corpus. |
ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models (2025.emnlp-main)
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| Challenge: | Experimental results show ToM outperforms existing divide-and-conquer frameworks . RAG relies on similarity-based rankings to retrieve and reason over chunks based on logical coherence . |
| Approach: | They propose a Tree-oriented MapReduce framework for long-context reasoning . it leverages the hierarchical structure of long documents by constructing a DocTree . |
| Outcome: | Experimental results show that ToM outperforms existing divide-and-conquer frameworks and RAGs . the proposed framework improves logical coherence and long-context reasoning on 70B+ LLMs compared to existing approaches . |
ArT: All-round Thinker for Unsupervised Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work on commonsense QA requires labeled training data for its success . existing work relies on large-scale in-domain or out-of-domain labeles or fails to generate knowledge of high quality in a general way. |
| Approach: | They propose an approach to commonsense question-answering (QA) that takes association during knowledge generation. |
| Outcome: | The proposed model outperforms existing models on commonsense QA benchmarks. |
Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (2022.coling-1)
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| Challenge: | Named entity recognition is a natural language processing task . nested NER is based on a linear structure, but there is no research on applying corpus-level information to NER. |
| Approach: | They propose a holistic structure parsing algorithm to reveal the entire NEs in a sentence . they introduce points-wise mutual information and other frequency features from corpus-aware statistics . |
| Outcome: | The proposed model outperforms existing models on widely-used benchmarks and achieves state-of-the-art. |
PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization (2025.findings-acl)
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| Challenge: | Existing LLM agents generate verbose and inefficient natural language plans to guide reasoning, which restricts agents’ ability to generalize across similar tasks. |
| Approach: | They propose a pseudocode-style planning guide optimization method that captures the structural logic of reasoning and uses two planning-oriented rewards to enhance agent learning. |
| Outcome: | The proposed method outperforms existing LLM agents on representative agent benchmarks and outperformed the current leading baselines. |
Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)
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| Challenge: | Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks. |
| Approach: | They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem. |
| Outcome: | Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models. |
Dissecting Human and LLM Preferences (2024.acl-long)
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| Challenge: | a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation. |
| Approach: | They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition. |
| Outcome: | The proposed model is compared with 32 different large language models using real-world user-model conversations. |
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
| Outcome: | The proposed method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance. |
PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference (2024.findings-acl)
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| Challenge: | Existing methods to reduce memory usage for large language models neglect inter-layer dependency between layers and huge memory consumption in pre-computation. |
| Approach: | They propose a method that compresses the KV cache by layer-wise retaining crucial context. |
| Outcome: | The proposed method reduces memory usage by layer-wise retaining crucial context . it can improve 2.2x throughput compared to Accelerate with over 54% memory reduction . |
ParaCook: On Time-Efficient Planning for Multi-Agent Systems (2026.findings-acl)
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Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang
| Challenge: | Existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. |
| Approach: | They propose a framework for large language models that allows agents to plan long-horizon tasks in a scalable way. |
| Outcome: | The proposed framework is based on the Overcooked game and can be used to evaluate time efficiency-aware multi-agent planning. |
LaCo: Large Language Model Pruning via Layer Collapse (2024.findings-emnlp)
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| Challenge: | Existing methods for model quantization, knowledge distillation, and model pruning are limited by hardware support limitations and the need for extensive training. |
| Approach: | They propose a layer-wise structured pruner that collapses rear model layers into a prior layer and enables a rapid reduction in model size while preserving the model structure. |
| Outcome: | The proposed pruner outperforms state-of-the-art pruning methods at pruning ratios of 25-30% and maintains an average task performance of over 80% at different pruning ratio. |
CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation (2024.findings-acl)
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| Challenge: | Current vital challenges for autonomous agents lie in two aspects: dependence on strong (M)LLMs and insufficient GUI environment modeling. |
| Approach: | They propose a comprehensive cognitive LLM agent with two novel approaches to improve GUI automation performance. |
| Outcome: | The proposed agent achieves state-of-the-art performance on AITW and META-GUI benchmarks. |
Faster In-Context Learning for LLMs via N-Gram Trie Speculative Decoding (2025.emnlp-main)
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| Challenge: | In-Context Learning (ICL) is a key method in prompt engineering, but its long retrieved contexts and limited token throughput will slow reasoning speeds. |
| Approach: | They propose a method that leverages the overlap between context and model output to generate drafts from the context. |
| Outcome: | The proposed method achieves the highest mean speedup on Vicuna-7B, Llama2-7B-Chat, and Llma3-8B-Instruct tasks. |
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model (2021.findings-acl)
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| Challenge: | Existing methods to exploit PrLMs for NLG tasks do not get as much performance gain as in the NLU task. |
| Approach: | They propose a method to integrate public checkpoints of PrLMs for the most convenience. |
| Outcome: | The proposed method significantly improves the quality of the language generation tasks on 6 different kinds of PrLMs. |
Syntax-aware Multilingual Semantic Role Labeling (D19-1)
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| Challenge: | Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation. |
| Approach: | They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. |
Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration (2025.emnlp-main)
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| Challenge: | Recent methods address Chinese Spelling Correction (CSC) with either BERT-based models or large language models (LLMs) however, both of them face challenges. |
| Approach: | They propose a model collaboration pipeline to iteratively optimize a BERT-based corrector. |
| Outcome: | The proposed model outperforms existing methods and outperformed human annotation methods. |
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (P19-1)
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| Challenge: | Head-driven phrase structure grammars have a uniform formalism representing rich contextual syntactic and even semantic meanings. |
| Approach: | They propose to integrate constituent and dependency formal representations into head-driven phrase structure. |
| Outcome: | The proposed parser achieves state-of-the-art performance on Penn Treebank and Chinese Penn TreeBank. |
One-shot Learning for Question-Answering in Gaokao History Challenge (C18-1)
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| 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. |
Automatic Article Commenting: the Task and Dataset (P18-2)
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| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
| Outcome: | The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models. |
Time-Aware Language Modeling for Historical Text Dating (2023.findings-emnlp)
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| Challenge: | Existing approaches to automatic text dating ignore diachronic change of words, which may affect the efforts of text modeling. |
| Approach: | They propose a time-aware language model to learn temporal word representations by transferring language models of general domains to those of time-specific ones and build a hierarchical modeling approach to represent diachronic documents by encoding them with temporal representations. |
| Outcome: | The proposed model outperforms state-of-the-art approaches in historical text dating and other NLP tasks. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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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 . |
Attention Is All You Need for Chinese Word Segmentation (2020.emnlp-main)
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| Challenge: | Recent work on Chinese word segmentation has been concerned about the following three perspectives. |
| Approach: | They propose to use a greedy decoding algorithm to improve Chinese word segmentation model. |
| Outcome: | The proposed model achieves state-of-the-art or comparable performance against strong baselines in strict closed test setting. |
Sentence Representation Learning with Generative Objective rather than Contrastive Objective (2022.emnlp-main)
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| Challenge: | Existing sentences-level training objectives focus on acquiring sentence-level representations, but they lack effective self-supervised objectives. |
| Approach: | They propose a generative self-supervised learning objective based on phrase reconstruction to improve sentence representation. |
| Outcome: | Empirical results show that the proposed objective outperforms current methods on STS benchmarks and retrieval and reranking tasks. |
Deep Enhanced Representation for Implicit Discourse Relation Recognition (C18-1)
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| Challenge: | Discourse parsing requires understanding of text spans and can't be easily derived from surface features from sentence pairs. |
| Approach: | They propose a model augmented with different grained text representations to improve discourse relation recognition. |
| Outcome: | The proposed model achieves state-of-the-art accuracy with greater than 48% in 11-way and F1 score greater than 50% in 4-way classifications for the first time according to our best knowledge. |
Rethinking Masked Language Modeling for Chinese Spelling Correction (2023.acl-long)
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| Challenge: | Existing CSC models over-fit the error model while under-fitting the language model, resulting in poor generalization to out-of-distribution error patterns. |
| Approach: | They propose to use a multi-domain benchmark LEMON to assess the open domain generalization of Chinese Spelling Correction models. |
| Outcome: | The proposed method achieves state-of-the-art results on SIGHAN, ECSpell, and LEMON. |
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution (2026.findings-acl)
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| Challenge: | Existing frameworks treat memory as a static append-only archive . Existing systems focus on passive accumulation, resulting in a 'passive accumulation' of memory. |
| Approach: | They propose a framework for experience-driven agent evolution that integrates procedural memory with contextual information to create a high-quality experience pool. |
| Outcome: | Experiments on BFCL-V3 and AppWorld show that ReMe outperforms memoryless Qwen3-8B. |
IAM: Efficient Inference through Attention Mapping between Different-scale LLMs (2025.acl-long)
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| Challenge: | Large language models (LLMs) are a challenge due to their internal reasoning processes. |
| Approach: | They propose an algorithm that can optimize attention matrices by performing attention mapping between small and large LLMs. |
| Outcome: | The proposed framework can reduce KV cache usage by 22.1% and accelerate prefill by 15% without sacrificing performance. |
CMMLU: Measuring massive multitask language understanding in Chinese (2024.findings-acl)
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| 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. |
Restricted or Not: A General Training Framework for Neural Machine Translation (2022.acl-srw)
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| Challenge: | Existing work imposes constraints on beam search decoding, which limits the concurrent processing ability of the model in deployment. |
| Approach: | They propose a general training framework that allows a model to support both restricted and unrestricted translations by adopting an additional auxiliary training process without constraining the decoding process. |
| Outcome: | The proposed training framework is tested on simulated and original benchmarks. |
Reference Language based Unsupervised Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Existing approaches to use a common language as an auxiliary for better translation have a long tradition in machine translation. |
| Approach: | They propose a reference language-based framework for unsupervised neural machine translation that uses only one auxiliary language as an auxiliary for better translation. |
| Outcome: | The proposed framework improves the quality of pivot translation over a baseline that uses only one auxiliary language. |
Attack Named Entity Recognition by Entity Boundary Interference (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is a cornerstone natural language processing task . despite its robustness, studies on its robustity are lacking. |
| Approach: | They propose a one-word modification NER attack that strategically inserts a new boundary into the sentence and triggers the model to make a wrong recognition. |
| Outcome: | The proposed method is effective on English and Chinese models with 70%-90% success rate. |
XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. however, their extensive memory requirements present significant challenges for deployment in resource-constrained environments. |
| Approach: | They propose a training-free framework that achieves ultra-low equivalent bit-width KV cache quantization. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on TruthfulQA and LongBench. |
Reorder and then Parse, Fast and Accurate Discontinuous Constituency Parsing (2022.emnlp-main)
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| Challenge: | Discontinuous constituency parsing is still being developed for its efficiency and accuracy are far behind its continuous counterparts. |
| Approach: | They propose to transform a discontinuous constituent tree into a pseudo-continuous one by reordering words in the sentence. |
| Outcome: | The proposed method can transform a discontinuous constituent tree into a pseudo-continuous one by parsing and performing actions on three classical discontinuous constituency treebanks. |
Unveiling Vulnerability of Self-Attention (2024.lrec-main)
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| Challenge: | Existing studies focus on manipulating word inputs, but they lack generalization to versatile real-world attacks. |
| Approach: | They propose a powerful perturbation technique which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. |
| Outcome: | The proposed perturbation technique achieves high attack success rate (98%) and low cost. |
Semantic Role Labeling with Associated Memory Network (N19-1)
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| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)
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| 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. |
Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification (2023.findings-acl)
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| Challenge: | Existing studies focus on grasping unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. |
| Approach: | They propose a commonsense fact verification model that bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. |
| Outcome: | The proposed model can bridge heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. |
Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing models only consider the unidirectional delivery of information from innermost layers to outer ones, but instead focus on nested entities. |
| Approach: | They propose a bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER) the bipartites are bidirectional LSTM and graph convolutional network (GCN) they first use the entities recognized by the flat NER module to construct an entity graph . |
| Outcome: | The proposed model outperforms existing models on three standard nested NER datasets. |
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association (P18-4)
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| Challenge: | a pinyin input method engine (IME) allows users to input Chinese into a computer by typing pinyan through the common keyboard. |
| Approach: | They present a pinyin IME that integrates neural machine translation and IR to offer amusive and customizable association ability. |
| Outcome: | The Moon IME integrates neural machine translation and IR to offer amusive association ability. |
Defending Pre-trained Language Models from Adversarial Word Substitution Without Performance Sacrifice (2021.findings-acl)
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| Challenge: | Existing defense approaches suffer from notable performance loss and complexities. |
| Approach: | They propose a framework for detecting anomalies with frequency-aware randomization to defend adversarial word substitution. |
| Outcome: | The proposed framework outperforms existing defense methods over various tasks with much higher inference speed. |
DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression (2025.acl-long)
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| Challenge: | Existing methods rely on information entropy as the metric to compress lexical units, but ignore attention-critical tokens and information . recent advent of In-Context Learning (ICL), Chain-of-Thought (CoT), and Retrieval Augmented Generation (RAG) technologies has significantly invigorated the landscape of applications based on Large Language Models (LLMs). |
| Approach: | They propose a dynamic attention-aware approach to task-agnostic prompt compression . they integrate entropy and attention information to achieve fine-grained prompt compression. |
| Outcome: | Experiments show that the proposed approach improves across tasks and LLMs. |
MiSS: An Assistant for Multi-Style Simultaneous Translation (2021.emnlp-demo)
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| Challenge: | MiSS is a multi-style simultaneous translation assistant . it has five key features: high translation accuracy, simultaneous translation, flexibility, and measurable translation quality. |
| Approach: | They propose an assistant system for multi-style simultaneous translation that provides a complete translation experience for machine translation users. |
| Outcome: | The proposed system improves translation efficiency and performance by combining machine translation, grammatical error correction, and interactive edits. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Chinese Spelling Corrector Is Just a Language Learner (2024.findings-acl)
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| Challenge: | a recent study shows that self-supervised learning can improve Chinese spelling correction by removing errors from training data. |
| Approach: | They propose a method that decodes Chinese spelling correction models using noise . they say it outperforms current methods that rely on annotated errors . |
| Outcome: | The proposed method outperforms the confusion set in specific domains because there are no errors in the training data. |
Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)
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| Challenge: | Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages. |
| Approach: | They propose a sign language interface that enables the DHH community to engage more fully with data analysis. |
| Outcome: | The proposed interface can be used by the deaf and hard-of-hearing community. |
Pre-training Universal Language Representation (2021.acl-long)
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| Challenge: | Despite the cutting-edge representation learning, most language models focus on specific levels of linguistic units. |
| Approach: | They propose a training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by an algorithm for pre-trained language models. |
| Outcome: | The proposed model achieves highest accuracy on analogy tasks in different language levels and significantly improves performance on downstream tasks. |
OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)
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| Challenge: | Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). |
| Approach: | They propose a new embedding approach based on the physical phenomenon of optical interference to reduce the semantic ambiguity in KGs. |
| Outcome: | The proposed model can compete with existing methods on KG benchmarks. |
Exploring Recombination for Efficient Decoding of Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) decoder captures features of entire prediction history . some partial hypotheses with different prefixes will be regarded differently no matter how similar they are . |
| Approach: | They propose a method that uses a n-gram suffix to adapt it to beam search decoding. |
| Outcome: | The proposed method can obtain similar translation quality with a smaller beam size, making it more efficient. |
Code Summarization with Structure-induced Transformer (2021.findings-acl)
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| Challenge: | Code summarization (CS) is a promising area in recent language understanding . previous work using structurebased traversal or non-sequential models to learn structural program semantics has shown no performance gain . |
| Approach: | They propose to use a structure-based traversal model to learn structural program semantics to generate human language automatically for programming language in the format of source code. |
| Outcome: | Experiments show that the proposed method achieves state-of-the-art on benchmarks. |
From Role-Play to Drama-Interaction: An LLM Solution (2024.findings-acl)
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| Challenge: | aristotle defined drama as a form of storytelling that involves a predefined storyline, emotions and thoughts. |
| Approach: | They propose to use LLMs to create an immersive mode of storytelling . they propose to create a backbone drama LLM to drive the playing process . |
| Outcome: | The proposed model can be used to drive the playing process, the authors say . it can be compared with existing models and can be evaluated on multiple scenarios. |
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval (2022.acl-long)
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| Challenge: | Existing studies focus on improving negative sampling strategy or extra pretraining for dense passage representations, but these studies are not capturing passage with internal representation conflicts. |
| Approach: | They propose a model with a smaller granularity to capture internal representation conflicts . they introduce a negative sampling strategy to encourage a diverse generation of sentence representations within the same passage. |
| Outcome: | The proposed model can be trained on three benchmark datasets to alleviate internal representation conflicts. |
Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)
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| Challenge: | Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood. |
| Approach: | They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget. |
| Outcome: | The proposed skipping policy can provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms. |
Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)
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| Challenge: | Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances. |
| Approach: | They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three multi-turn conversation benchmarks including an e-commerce dialogue corpus. |
From Parameters to Performance: A Data-Driven Study on LLM Structure and Development (2025.emnlp-main)
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| Challenge: | Large language models have revolutionized a wide range of domains, driving significant advancements in both technology and real-world applications. |
| Approach: | They present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks. |
| Outcome: | The proposed model validates the relationship between structural configurations and performance across multiple benchmarks and further corroborates the findings using mechanistic interpretability techniques. |
Span Fine-tuning for Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to fine-tune pre-trained language models are time-consuming and lack flexibility. |
| Approach: | They propose a span fine-tuning method which allows for a more efficient and efficient way of incorporating span-level information into pre-training. |
| Outcome: | Experiments on GLUE benchmark show that the proposed method significantly enhances the PrLM and offers more flexibility in an efficient way. |
BoYaEval: Evaluating Multimodal Large Language Models on Understanding Ancient Chinese Musical Scores (2026.acl-long)
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| Challenge: | Multimodal Large Language Models excel in general tasks but struggle with specialized, structured cultural symbols. |
| Approach: | They evaluate 21 leading MLLMs and compare their performance to a benchmark for Ancient Chinese musical notation. |
| Outcome: | The benchmark evaluates 21 leading MLLMs on five types of ancient Chinese music notation systems. |
Seq2seq Dependency Parsing (C18-1)
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| Challenge: | Recent trend for dependency parsing is adopting neural networks due to their significant success in a wide range of applications. |
| Approach: | They propose a sequence to sequence (seq2seque) dependency parser that predicts the relative position of head for each word. |
| Outcome: | The proposed parser achieves 94.11% UAS on PTB and 88.78% UAS . |
Contextualized Semantic Distance between Highly Overlapped Texts (2023.findings-acl)
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| Challenge: | Conventional semantic metrics are based on word representations and are vulnerable to disturbance of overlapped components with similar representations. |
| Approach: | They propose a mask-and-predict strategy to evaluate the semantic distance between the overlapped sentences using words in the longest common sequence as neighboring words and use masked language modeling to predict their positions. |
| Outcome: | The proposed method outperforms the state-of-the-art in domain adaption by a huge margin. |
Enhancing Multimodal Unified Representations for Cross Modal Generalization (2025.findings-acl)
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Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Minghui Fang, Jieming Zhu, Zhenhua Dong, Sashuai Zhou, Zhou Zhao
| Challenge: | Existing studies on discrete unified representations overlook important distinctions between different dimensions of features. |
| Approach: | They propose to use a codebook to optimize unified representations from pretraining and fine- and coarse-grained disentangling to optimize the representations. |
| Outcome: | The proposed methods improve the interpretability of multimodal unified representations . they use training-free optimization of codebook and fine and coarse cross-modal disentangling . |
LIMIT-BERT : Linguistics Informed Multi-Task BERT (2020.findings-emnlp)
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| Challenge: | Existing language models are usually trained on large amounts of unlabeled text data. |
| Approach: | They propose a multi-task language representations learning framework for multi-linguistics tasks by Multi-Task Learning. |
| Outcome: | The proposed model outperforms the baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task. |
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation (2021.naacl-industry)
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| Challenge: | Existing models that use only monolingual data have not been fully duplicated in the vast majority of language pairs, especially for zero-source languages. |
| Approach: | They propose to leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. |
| Outcome: | The proposed model significantly improves translation quality with a big margin in the benchmark unsupervised translation tasks and achieves comparable performance to supervised NMT. |
TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to autoregressive reasoning . SpecReason adopts a polling-based design that repeatedly invokes the LRM for verification at every step . |
| Approach: | They propose a trigger-based collaborative reasoning framework that delegates most reasoning to the SRM and activates LRM intervention only when necessary. |
| Outcome: | The proposed framework reduces latency and API cost by 73.3% under edge–cloud conditions. |
The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models (2024.findings-acl)
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| Challenge: | Existing benchmarks to evaluate LLMs' capabilities are inadequate for assessing their musical capabilities. |
| Approach: | They propose to use a large-scale music benchmark specifically designed to evaluate the music-related capabilities of large language models (LLMs). |
| Outcome: | The proposed framework evaluates 16 large language models in the domain of music. |
ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)
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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng, Zhou Zhao
| Challenge: | Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation. |
| Approach: | They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space. |
| Outcome: | The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions. |
Structural Pre-training for Dialogue Comprehension (2021.acl-long)
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| Challenge: | Recent advances in large-scale pre-training language models (PrLMs) have achieved remarkable successes in a variety of natural language processing tasks. |
| Approach: | They propose to use SPIDER to capture dialogue exclusive features from dialogue texts. |
| Outcome: | The proposed model performs well on widely used dialogue benchmarks. |
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |
Driving Chinese Spelling Correction from a Fine-Grained Perspective (2025.coling-main)
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| Challenge: | Existing evaluations for Chinese spelling correction lack nuanced typology for spelling errors, creating an "invisible" bottleneck . |
| Approach: | They propose a fine-grained evaluation principle for Chinese spelling correction (CSC) they categorize spelling errors into six different types and use it to evaluate models . |
| Outcome: | The proposed evaluation principle can be leveraged to enhance CSC training models. |
Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing approaches to aspect-based sentiment analysis stack multiple modules and result in severe error propagation. |
| Approach: | They propose a MRC-PrOmpt mOdeL framework where multiple sentiment aspects are elicited by a machine reading comprehension model and their corresponding sentiment polarities are classified in a prompt learning way. |
| Outcome: | The proposed framework significantly outperforms existing state-of-the-art models or achieves comparable performance on widely-used benchmark datasets. |
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)
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| Challenge: | Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges. |
| Approach: | They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries . |
| Outcome: | The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs. |
On the Robustness of Editing Large Language Models (2024.emnlp-main)
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| Challenge: | Existing studies have exhibited impressive success and significant potential. |
| Approach: | They propose to modify the knowledge memory with minimum computational cost while preserving the performance on the retained knowledge. |
| Outcome: | The proposed methods avoid retraining to update the model parameters and have demonstrated promising performance and efficiency. |
SirLLM: Streaming Infinite Retentive LLM (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are becoming increasingly prevalent in various domains, requiring a one-off input of overly long texts to maintain a degree of memory. |
| Approach: | They propose a Streaming Infinite Retentive LLM which allows LLMs to maintain longer memory during infinite-length dialogues without fine-tuning. |
| Outcome: | The proposed model can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. |
AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing methods for large language models (LLMs) lean on handcrafted or task-specific demonstrations and lack reliable knowledge base. |
| Approach: | They propose a one-for-all platform for augmented reasoning and refining based on chain-of-thought prompting that excels in adaptability, reliability, integrity, and interpretability. |
| Outcome: | The proposed system exhibits superior performances across six reasoning tasks and offers real-time visual analysis. |
Learning Better Masking for Better Language Model Pre-training (2023.acl-long)
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| Challenge: | Existing PrLMs adopt a Random-Token Masking strategy with a fixed masking ratio and different contents are masked by an equal probability throughout the training. |
| Approach: | They propose two scheduled masking approaches that adaptively tune masking ratio and masked content in different training stages, which improves pre-training efficiency and effectiveness. |
| Outcome: | The proposed methods improve the pre-training efficiency and effectiveness on the downstream tasks. |
Syntax for Semantic Role Labeling, To Be, Or Not To Be (P18-1)
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| Challenge: | Existing neural SRL models lack syntactic backbone for performance, limiting its use in deep learning. |
| Approach: | They propose an enhanced argument labeling model with extended korder argument pruning algorithm for effectively exploiting syntactic information. |
| Outcome: | The proposed model achieves state-of-the-art on the CoNLL-2008 and 2009 benchmarks in English and Chinese. |
Unsupervised Neural Machine Translation with Universal Grammar (2021.emnlp-main)
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| Challenge: | Unsupervised machine translation relies on parallel corpora for training, but performance still lags behind traditional supervised machine translators. |
| Approach: | They propose to leverage shared grammar clues to provide more explicit language parallel signals to enhance the training of unsupervised machine translation models. |
| Outcome: | The proposed models improve on a common language pair training task in English and german, and use embedding alignments and pretrained language models to synthesize pseudo parallel corpora. |
Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet (D18-1)
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| Challenge: | Chinese pinyin input method engine (IME) converts pinyine into character based on its core component, pinyan-to-character conversion (P2C). |
| Approach: | They propose a sequence-to-sequence model with gated-attention mechanism for Chinese IMEs. |
| Outcome: | The proposed model improves on existing models in benchmark datasets showing great user experience improvement compared to traditional models. |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
Selective Prefix Tuning for Pre-trained Language Models (2024.findings-acl)
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| Challenge: | Existing methods for fine-tuning pre-trained models are time-consuming and memory-inefficient. |
| Approach: | They propose a method that inserts learnable vectors into each Transformer layer . they propose SL to encourage diversity in prefix tokens . |
| Outcome: | Extensive experiments validate the effectiveness of Prefix Tuning in sentence and token classification tasks. |
Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
| Approach: | tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks. |
| Outcome: | This tutorial will introduce the background and the latest progress of syntactic parsing and SRL/NMT. |
VHASR: A Multimodal Speech Recognition System With Vision Hotwords (2024.emnlp-main)
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| Challenge: | Existing models that incorporate audio-related image information do not improve speech recognition performance. |
| Approach: | They propose a novel approach utilizing audio-related image information and set up a multimodal speech recognition system that uses vision as hotwords to enhance the model’s speech recognition capability. |
| Outcome: | The proposed model outperforms unimodal ASR model and achieves SOTA among existing image-based multimodal ASL models. |
LESA: Learnable LLM Layer Scaling-Up (2025.acl-long)
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| Challenge: | Existing methods for depth scaling-up rely on empirical heuristic rules for layer duplication, resulting in poor initialization and slower convergence during continual pre-training. |
| Approach: | They propose a method for learning latent parameters between layers by concatenating parameters from each layer and applying Singular Value Decomposition. |
| Outcome: | Experiments show that LESA outperforms baseline models with less than half the cost of existing methods. |
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for multi-party dialogue machine reading comprehension focus on how to incorporate speaker information into the model, which is usually rare in real scenarios. |
| Approach: | They propose to model speaker and key-utterances using self-supervised prediction tasks and capture salient clues in a long dialogue. |
| Outcome: | The proposed method outperforms baseline models and state-of-the-art models on two benchmark datasets. |
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Recent studies on adversarial attacks achieve high success rates against PrLMs, claiming that they are not robust. |
| Approach: | They propose to use anomaly detector to evaluate PrLMs with more natural adversarial samples to evaluate their robustness. |
| Outcome: | The proposed method can be used to defend all types of attacks and achieve higher accuracy on adversarial samples and compliant samples than other defense frameworks. |
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable capabilities by leveraging chain-of-thought reasoning techniques to solve complex questions. |
| Approach: | They propose a method that assesses the entailment relationship between the question and the candidate reasoning chain and uses it to predict the answer. |
| Outcome: | The proposed approach improves the fine-tuned T5 baseline over the ScienceQA, ECQA, and LastLetter tasks. |
EM Pre-training for Multi-party Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to pretrain large language models for dialogue response generation are difficult due to the lack of annotated addressee labels in multi-party dialogue datasets. |
| Approach: | They propose an Expectation-Maximization approach that iteratively performs expectation steps to generate addressee labels and maximize a response generation model. |
| Outcome: | The proposed method is based on two-party dialogues and multi-party dialogs. |
Instruction-Driven Game Engine: A Poker Case Study (2024.emnlp-demo)
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| Challenge: | a new concept allows users to create games by natural language instructions . the concept is based on the text-based game states, which are rendered to visual display . |
| Approach: | They propose an instruction-driven game engine that allows users to create games by natural language instructions. |
| Outcome: | The proposed concept allows users to create games simply by natural language instructions . initial progress lies in developing an IDGE for poker, which supports a wide range of poker variants . |
Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions (2025.acl-long)
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| Challenge: | Experimental results show that multimodal GUI agents are susceptible to environmental distractions. |
| Approach: | They propose a scenario where both user and agent are benign and environment is not malicious . they implement an adversarial environment injection and analyze the approach to improve faithfulness . |
| Outcome: | The proposed approach improves faithfulness of multimodal large language model agents in a graphical user interface environment. |
Semantic-Preserving Adversarial Code Comprehension (2022.coling-1)
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| Challenge: | Existing studies on improving PrLMs for source code comprehension have not found a way to improve both sides of the trade-off between the two aspects. |
| Approach: | They propose to use semantic-preserving code embeddings to find worst-case attacks while forcing the model to predict the correct labels under these worst cases. |
| Outcome: | The proposed model can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code. |
High-order Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
| Outcome: | The proposed model can explicitly consider the isolated predicate-argument pairs and the interaction between the predicates-argoments pairs. |
Open-Theatre: An Open-Source Toolkit for LLM-based Interactive Drama (2025.emnlp-demos)
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| Challenge: | Existing tools for creating, modifying, and experimenting with interactive dramas are limited. |
| Approach: | They propose an open-source toolkit for creating configurable LLM-based interactive drama. |
| Outcome: | The proposed toolkit enhances narrative coherence and realistic behavior in interactions with agents. |
A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware? (C18-1)
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| Challenge: | Existing models for semantic role labeling are syntax-agnostic, but outperform them on benchmarks. |
| Approach: | They propose an end-to-end neural model which tackles the SRL problem in one shot . they augment the encoder with a non-linear transformation to distinguish the predicate and the argument . |
| Outcome: | The proposed model outperforms state-of-the-art syntax-aware SRL systems on CoNLL-2008 and 2009 benchmarks for English and Chinese. |
Subword-augmented Embedding for Cloze Reading Comprehension (C18-1)
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| Challenge: | Existing models for machine reading comprehension use word and character representations, but character is not the minimal unit. |
| Approach: | They propose to use subword rather than character for word embedding enhancement . they also empirically explore different augmentation strategies on subword-augmented embedded embedders . |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets. |
GoT: Effective Graph-of-Thought Reasoning in Language Models (2024.findings-naacl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have been advancing at an unprecedented pace. |
| Approach: | They propose a graph-based approach which models human thought processes as a chain and as 'graphs' by representing thought units as nodes and connections between them as edges, they capture the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. |
| Outcome: | The proposed model improves on a text-only reasoning task and a multimodal reasoning task. |
Open Vocabulary Learning for Neural Chinese Pinyin IME (P19-1)
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| Challenge: | Pinyin-to-character conversion is the core component of pinyin based Chinese input method engine (IME). |
| Approach: | They propose a neural P2C conversion model augmented by an online updated vocabulary to support open vocabulary learning during IME working. |
| Outcome: | The proposed model outperforms commercial IMEs and state-of-the-art models on standard corpus and true inputting history dataset in terms of multiple metrics and the online updated vocabulary helps it follow user inputting behavior. |
Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)
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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
| Challenge: | Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. |
| Approach: | They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. |
| Outcome: | The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks. |
GLaPE: Gold Label-agnostic Prompt Evaluation for Large Language Models (2024.emnlp-main)
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| Challenge: | Recent studies have explored leveraging the LLM itself as an optimizer to identify optimal prompts that maximize task accuracy. |
| Approach: | They propose a gold label-agnostic prompt evaluation method to reduce dependence on gold labels. |
| Outcome: | The proposed method produces more effective prompts even without gold labels. |
GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.findings-acl)
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| Challenge: | Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence . |
| Approach: | They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art models with reduced token overhead. |
Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to measure word segmentation only assess the language model's understanding of the overall meaning of sentences, lacking an evaluation of the language models' understanding capabilities at a fine-grained level. |
| Approach: | They propose a framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) they employ current mainstream LLMs to perform word segmentations across multiple languages . |
| Outcome: | The proposed method improves on existing methods and combines the advanced pattern recognition capabilities of Aho-Corasick automata with the deep insights of well-pretrained LLMs. |
Are LLMs Aware that Some Questions are not Open-ended? (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive capability to answer questions in a wide range of scenarios. |
| Approach: | They propose a method that enhances the question awareness of LLMs by adaptively adjusting the output distributions based on question features. |
| Outcome: | The proposed method improves the question awareness of LLMs by adaptively adjusting the output distributions based on question features. |
Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning (2023.findings-emnlp)
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| Challenge: | Existing open-domain question-answering methods lack quality assurance . existing methods lack scalability and poor diversity, hindering LLMs' capabilities . |
| Approach: | They propose an open-domain multi-hop reasoning framework to answer multi-choice questions . they propose an adaptive sampler for in-context selection and self-prompted inference . |
| Outcome: | The proposed framework surpasses the existing SOTA methods on large-scale datasets and doubles the zero-shot performance of small-scale LLMs. |
PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing summarization strategies are abstractive and extractive, but are hard to control. |
| Approach: | They propose a PhRase-level cOpying Mechanism that enhances attention on n-grams and calculates an auxiliary loss for the copying prediction. |
| Outcome: | Empirical studies show that PROM improves copying accuracy and faithfulness on benchmarks. |
Game Development as Human-LLM Interaction (2025.acl-long)
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| Challenge: | Currently, game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. |
| Approach: | They propose a chat game engine powered by LLM that allows everyone to develop a custom game using natural language through Human-LLM interaction. |
| Outcome: | The proposed engine is designed to support the development of custom games using natural language through Human-LLM interaction. |
Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
Lingke: a Fine-grained Multi-turn Chatbot for Customer Service (C18-2)
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| Challenge: | e-commerce chatbots usually need a mass of human dialogue data to train, but for multi-turn conversations, the performance is poor. |
| Approach: | They propose an information retrieval augmented multi-turn chatbot which can answer questions based on unstructured documents and deal with multi-turned conversations. |
| Outcome: | The proposed solution outperforms all other models in multi-turn conversations and can learn from conversation records. |
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data. |
| Approach: | They propose a method to generate huge truthful and customized dialogues without worrying about factual errors caused by the model hallucination. |
| Outcome: | The proposed method solves the model hallucination in dialogue generation by restricting the LLMs to leverage the given reference instead of reciting their own knowledge to generate dialogues. |
Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. |
| Approach: | They propose a lightweight IUR model for query rewriting to complete key information in dialogue to enhance retrieval. |
| Outcome: | The proposed model improves retrieval and generation ability of RAG system in multi-round dialogue scenarios. |
Advances and Challenges in Unsupervised Neural Machine Translation (2021.eacl-tutorials)
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| Challenge: | Unsupervised neural machine translation (UNMT) has achieved impressive results, but there are still several challenges for the technology. |
| Approach: | They present a framework for unsupervised neural machine translation (UNMT) they examine the latest progress and challenges of UNMT and examine how it holds up . |
| Outcome: | The proposed method has achieved impressive results but still faces challenges. |
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)
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| Challenge: | a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents . |
| Approach: | They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions. |
| Outcome: | The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round. |
MEGen: Generative Backdoor into Large Language Models via Model Editing (2025.findings-acl)
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| Challenge: | Existing methods for training large language models are limited to yes-or-no discriminative tasks, leading users to underestimate the potential risks. |
| Approach: | They propose an editing-based generative backdoor that expands the backdoor to generative tasks in a unified format of any text-to-any text. |
| Outcome: | The proposed model achieves high attack success rate by adjusting only a small set of local parameters with few-shot samples. |
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information. |
| Approach: | They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens. |
| Outcome: | The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision. |
KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding (2025.acl-long)
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| Challenge: | Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. |
| Approach: | They propose a paradigm called KV-Latent to reduce the KV cache footprint and improve inference speed by down-sampling the Key-Value vector dimensions into a latent space. |
| Outcome: | The proposed paradigm reduces the KV Cache footprint and improves inference speed with a small amount of extra training, less than 1% of pre-training takes. |
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning (2023.emnlp-main)
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| Challenge: | a new self-supervised learning method allows language models to generalize to much harder and longer-hop logic. |
| Approach: | They propose a self-supervised learning method to empower language models with boolean logic . they augment training data with nested booles and program training from simpler to harder ones . |
| Outcome: | The proposed method allows language models to generalize to much harder and longer-hop logic, which can hardly be learned through naive training. |
PAR: Training-Free Positional Perturbation and Attention Recycling for Faithful OCR (2026.acl-long)
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| Challenge: | In high-precision tasks, vision language models suffer from Linguistic Priors Hallucination . |
| Approach: | They propose a training-free, inference-time intervention framework to mitigate this by integrating visual encoders with Large Language Model decoders. |
| Outcome: | The proposed framework reduces hallucination rates by 12% in long-context scenarios while maintaining robust generalization on standard benchmarks. |