Papers by Junhui Li
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
| Approach: | They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent. |
| Outcome: | The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks. |
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately. |
| Approach: | They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis. |
| Outcome: | The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. |
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings (P18-1)
Copied to clipboard
| Challenge: | Neural machine translation uses source and target word embeddings to improve translation quality . source and targeted word embeds are at the two ends of a long information processing procedure . |
| Approach: | They propose a method to shorten the distance between source and target words in neural machine translation by bridging source and targeting word embeddings. |
| Outcome: | The proposed method shortens the distance between source and target words in neural machine translation and strengthens their association. |
Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have revolutionized natural language processing (NLP) there is an urgent need for new benchmarks to keep pace with the development of LLMs. |
| Approach: | They propose a benchmark to assess the capability of large language models (LLMs) they use a dataset to provide both knowledge assessment and application assessment . |
| Outcome: | The proposed benchmark provides datasets tailored for knowledge assessment and application assessment. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
Copied to clipboard
Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
Copied to clipboard
| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
| Outcome: | The proposed model can be used to translate both sentences and documents on four translation tasks. |
POP: Prefill-Only Pruning for Efficient Large Model Inference (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing structured pruning methods suffer from significant accuracy degradation . Existing pruning methods are expensive and require specialized hardware and kernels to perform . |
| Approach: | They propose a stage-agnostic pruning approach that overlooks asymmetric roles between prefill and decode stages. |
| Outcome: | The proposed pruning approach achieves 1.37 speedup in prefill latency with minimal performance loss. |
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)
Copied to clipboard
Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang
| Challenge: | Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge . |
| Approach: | They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST . |
| Outcome: | The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance . |
MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing financial benchmarks rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. |
| Approach: | They propose a multilingual, multi-sector, and multi-task dataset called MFinMeeting that supports English, Chinese, and Japanese . |
| Outcome: | The proposed benchmark supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts. |
CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for activation sparsification do not capture the relationship between activation and model performance. |
| Approach: | They propose a general activation sparsification approach using channel-wise thresholding and selective sparsifying to capture the relationship between activation and model performance. |
| Outcome: | The proposed approach reduces the number of activated neurons during inference by 1.27x over eight downstream tasks while activating fewer parameters than existing methods. |
Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that consistency preference for lexical chains reduces lexical translation inconsistency . Lexical translation consistency is a common discourse phenomenon . |
| Approach: | They propose a consistency-aware model which captures consistency context . they then define consistency-tailored latent variables which guide translation of corresponding sentences . |
| Outcome: | The proposed model significantly improves translation performance in ChineseEnglish and FrenchEnglish translation tasks. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
Copied to clipboard
Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
| Approach: | They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization . |
| Outcome: | The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models . |
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators (2024.emnlp-main)
Copied to clipboard
| Challenge: | Concatenating large language models are adapted to context-aware neural machine translation in a concatenated way . a recent paradigm shift has been witnessed in discourse-related challenges such as zero pronoun translation . |
| Approach: | They propose an alternative adaptation approach to make large language models discriminately model and utilize inter- and intra-sentence contexts. |
| Outcome: | The proposed approach outperforms concatenation mode and improves performance in discourse modeling. |
Lexical Translation Inconsistency-Aware Document-Level Translation Repair (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show document-level translation repair improves translation consistency but still suffers from lexical translation inconsistency due to the lack of inter-sentence context. |
| Approach: | They propose a document-level translation repair model to model translation inconsistency via automatic post-editing. |
| Outcome: | The proposed model improves translation quality and lexical consistency on document-level translation datasets. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement (2025.acl-long)
Copied to clipboard
| Challenge: | Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. |
| Approach: | They propose to extend translation refinement from sentence-level to document-level by using document-to-document (Doc2Doc) translations. |
| Outcome: | The proposed method improves translation quality across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instru. |
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model . |
| Approach: | They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario. |
| Outcome: | The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario. |
Adaptive Weighting for Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
| Approach: | They propose adaptive weighting for WSMs to control the contribution of each input and output state. |
| Outcome: | The proposed weighting improves translation accuracy by 1.49 and 0.92 BLEU points on Chinese-to-English translation and English-to German translation tasks. |
A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)
Copied to clipboard
| Challenge: | Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory . |
| Approach: | They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation. |
| Outcome: | The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses. |
Evaluation Dataset for Lexical Translation Consistency in Chinese-to-English Document-level Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on document-level neural machine translation (NMT) assume that all repeated source words should be translated consistently. |
| Approach: | They construct a test set of 310 bilingual news articles to evaluate lexical translation consistency. |
| Outcome: | The proposed test sets show that translation consistency is consistent across multiple languages. |
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing faithful RAG approaches enforce strict context adherence, but they forcibly suppress the model’s parametric knowledge, which undermines the model's internal knowledge structure and increases the risk of misinterpreting the context. |
| Approach: | They propose a framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model’s parametric knowledge and retrieved context. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in knowledge conflict cases and identifies conflicting knowledge at the fact level and designs a self-thinking process. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
MLWQ: Efficient Small Language Model Deployment via Multi-Level Weight Quantization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for efficient deployment of small language models face inefficient bit-width allocation and insufficient fine-grained quantization adjustments. |
| Approach: | They propose a weight quantization technique that facilitates efficient deployment of SLMs . they propose to combine inter-layer loss and intra-layer salience to achieve better allocation . |
| Outcome: | Experimental results show that multi-level weight quantization achieves competitive performance compared to state-of-the-art methods. |
Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)
Copied to clipboard
Suhang Wu, Jialong Tang, Chengyi Yang, Pei Zhang, Baosong Yang, Junhui Li, Junfeng Yao, Min Zhang, Jinsong Su
| Challenge: | Existing methods for terminology translation struggle with interference from irrelevant noise. |
| Approach: | They propose a Locate-and-Focus method that locates terminologies within utterances to construct translation knowledge by minimizing irrelevant information for ST models. |
| Outcome: | The proposed method locates terminologies within utterances and enhances the success rate of terminology translation while maintaining robust general translation performance. |