Papers by Hongshen Chen
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)
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| Challenge: | Current state-of-the-art neural dialogue models learn from human conversations . however, due to the open-ended nature of human conversations, the quality of training data varies . |
| Approach: | They propose a data manipulation framework to augment and highlight effective training samples . they also propose to increase its manipulation skills through gradient descent with validation samples a reshaping framework to proactively restructure the data distribution towards reliable samples is also proposed . |
| Outcome: | The proposed framework improves the performance of open-domain neural dialogue models with respect to evaluation metrics and human judgments. |
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)
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| Challenge: | Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks. |
| Approach: | They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain. |
| Outcome: | The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs. |
Multilingual Brain Surgeon: Large Language Models Can Be Compressed Leaving No Language behind (2024.lrec-main)
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| Challenge: | Existing methods for MC focus on quantization and network pruning. |
| Approach: | They propose a calibration method that samples calibration data from various languages proportionally to the language distribution of the model training datasets. |
| Outcome: | The proposed method improves the performance of existing English-centric compression methods on the BLOOM multilingual LLM. |
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated. |
| Approach: | They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
| Outcome: | The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
Group-wise Contrastive Learning for Neural Dialogue Generation (2020.findings-emnlp)
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| Challenge: | Existing approaches to training dialogue models have low diversity in open-domain contexts . prior art suggests that naive MLE objective is not effective enough . |
| Approach: | They propose to incorporate contrastive learning into dialogue generation by using a pretrained baseline model as a reference. |
| Outcome: | The proposed framework is suited for training a wide range of dialogue generation models with favorable performance over baseline training approaches. |
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)
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| Challenge: | Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases. |
| Approach: | They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing. |
Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning. |
| Approach: | They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons. |
| Outcome: | The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process. |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition (2021.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack fine-grained control over knowledge selection and integration with dialogues. |
| Approach: | They propose to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. |
| Outcome: | The proposed model outperforms baseline models on knowledge-grounded dialogue benchmarks. |
Knowledge Diffusion for Neural Dialogue Generation (P18-1)
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| Challenge: | End-to-end neural dialogue generation does not employ knowledge to guide the generation. |
| Approach: | They propose a neural knowledge diffusion model to introduce knowledge into dialogue generation. |
| Outcome: | The proposed model outperforms baseline models on a real-world dataset. |
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
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| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art models in both content quality and emotion perceptivity. |
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought (2023.findings-emnlp)
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| Challenge: | Recent studies have focused on the development of semantic parsers within the framework of cross-domain analysis. |
| Approach: | They propose a method to generate auto-CoT exemplars using ACT-SQL and extend it to multi-turn text-to-Sql tasks. |
| Outcome: | The proposed method achieves SOTA performance on the Spider dev set among existing in-context learning approaches. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
| Approach: | They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation. |
| Outcome: | The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets. |
Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge (2025.findings-acl)
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| Challenge: | Temporal Logic (STL) is a formal specification tool for cyber-physical systems . but it is difficult to transform ambiguous and complex data into STL, a paper argues . |
| Approach: | They propose a NL-STL dataset with 16,000 samples enriched with diverse patterns . they propose KGST framework to transform natural language into STL using a generate-then-refine process . |
| Outcome: | The proposed dataset outperforms baseline models in diversity and accuracy . the proposed dataset contains 16,000 samples enriched with diverse patterns . |
Adaptive Bridge between Training and Inference for Dialogue Generation (2021.emnlp-main)
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| Challenge: | Experimental results show that our model can achieve a significant improvement in terms of metric-based evaluation and human evaluation compared with the state-of-the-art exposure bias approaches. |
| Approach: | They propose a novel adaptive switching mechanism which automatically transits between ground-truth learning and generated learning regarding the word-level matching score. |
| Outcome: | The proposed model improves on Chinese and English reddit datasets compared with state-of-the-art models on the word-level matching score. |
Unraveling the Mystery: Defending Against Jailbreak Attacks Via Unearthing Real Intention (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly vulnerable to elusive and implicit intentions, causing security risks and compromising user experience. |
| Approach: | They propose a method to detect and mitigate implicit jailbreak attacks using LLMs by unearthing real intentions and a greedy gradient-based algorithm to remove the least important parts of a sentence. |
| Outcome: | The proposed method reduces attacks success rate and Harmful Score while maintaining overall model performance. |
Regularizing Dialogue Generation by Imitating Implicit Scenarios (2020.emnlp-main)
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| Challenge: | Existing models for dialogue generation lack the flexibility to handle such freedoms. |
| Approach: | They propose to take into account dialogue history and future conversation to implicitly reconstruct the scenario knowledge. |
| Outcome: | The proposed approach outperforms state-of-the-art models on diversity and relevance and expresses scenario-specific knowledge. |
Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)
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| Challenge: | Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. |
| Approach: | They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results . |
| Outcome: | The proposed framework improves on two commonly-used open benchmarks and achieves state-of-the-art or competitive results while using less memory and having a lower inference latency. |
Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)
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| Challenge: | Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. |
| Approach: | They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme. |
| Outcome: | The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods. |
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)
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| Challenge: | Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem. |
| Approach: | They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content . |
| Outcome: | The proposed model outperforms baseline models on both SAMSum and DialSum datasets. |
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to abstractly summarize dialogues are limited to two or more interlocutors. |
| Approach: | They propose to use existing document summarization models to capture the various topic information of a conversation and outline salient facts for the captured topics. |
| Outcome: | The proposed method significantly outperforms baselines and achieves new state-of-the-art performance on benchmark datasets. |
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to knowledge-grounded dialogue generation perform relatively independent sub-tasks . Typical approaches tend to decompose this task into two streamlined sub- tasks . |
| Approach: | They propose a collaborative latent variable model to integrate knowledge selection and knowledge-aware response generation simultaneously in separate but collaborative latences. |
| Outcome: | The proposed model outperforms previous methods on knowledge selection and response generation. |