Papers by Hai Chen
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation (2025.findings-acl)
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| Challenge: | Traditional video topic segmentation methods struggle to discern topical transitions . supervised approaches have improved performance on video action or scene segmentation . |
| Approach: | They propose a new task for video topic segmentation that enhances multimodality alignment and fusion by exploring different architectures using Cross-Attention and Mixture of Experts. |
| Outcome: | The proposed model improves on educational videos, in the form of lectures . it combines cross-attention and mixture of experts to strengthen multimodality alignment and fusion . |
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling (2023.emnlp-main)
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| Challenge: | Recent supervised neural models have greatly promoted the development of topic segmentation, but the deeper relationship between coherence and topic segmenting is underexplored. |
| Approach: | They propose to use topic-aware Sentence Structure Prediction and Contrastive Semantic Similarity Learning to capture coherence from logical structure and semantic similarity perspectives to further improve topic segmentation performance. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection. |
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. |
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)
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Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy
| Challenge: | Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. |
| Approach: | They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement. |
| Outcome: | Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. |
Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)
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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin
| Challenge: | Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types. |
| Approach: | They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets. |
| Outcome: | The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning. |
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. |
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. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN (D18-1)
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| Challenge: | Recent research shows the strength of the Encoder-Decoder model in text summarization. |
| Approach: | They propose to use the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously and then to align the sentences and images in summaries. |
| Outcome: | The proposed model outperforms the existing methods that do not consider images . it can generate informative summaries of images, and it can be used to summarize documents . |
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. |
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection (2024.naacl-long)
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Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
| Challenge: | Instruction-tuned Large Language Models (LLMs) can modulate responses based on human instructions, but they can be maliciously steered to impact society in subtle but persistent ways. |
| Approach: | They propose a backdoor attack setting that allows an attacker to inject a virtual prompt into an LLM to steer it without any explicit injection at its input. |
| Outcome: | The proposed method is able to poison the model's instruction tuning data and show that it is highly effective in steering the model. |
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)
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| Challenge: | Existing studies focus on a single query language, resulting in limited generalizability . a new task paradigm is proposed to unify semantic parsing tasks across different query languages . |
| Approach: | They propose a task paradigm that unifies parsing tasks across query languages . they identify query skeletons as a shared optimization target of Text-to-Query tasks . |
| Outcome: | The proposed method achieves state-of-the-art performance using only a small amount of synthesized data. |
A DQN-based Approach to Finding Precise Evidences for Fact Verification (2021.acl-long)
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| Challenge: | Existing methods for fact verification do not target the retrieval of precise evidences. |
| Approach: | They propose a DQN-based approach to retrieval of precise evidences . they propose best thresholds for determining the true labels of computed evidences. |
| Outcome: | The proposed method improves accuracy of fact verification by reducing label bias . it can retrieve evidence consisting of the first two sentences, but it can contain unnecessary sentences . |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |
Jointly Identifying Rhetoric and Implicit Emotions via Multi-Task Learning (2021.findings-acl)
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| Challenge: | Experimental results validate the benefit of the proposed model over the state-of-the-art baselines for rhetoric and emotion identification tasks. |
| Approach: | They propose a multi-task learning framework that can encode categorical correlation between tasks to improve rhetoric and emotion identification problem. |
| Outcome: | The proposed model can encode the categorical correlation between tasks to improve rhetoric and emotion identification problem. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)
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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
| Challenge: | Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning. |
| Approach: | They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. |
| Outcome: | Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks. |
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates (2022.acl-long)
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| Challenge: | Existing methods for enhancing pre-trained cross-lingual language models with additional data are rare in practice, especially for low-resource languages. |
| Approach: | They propose a prompt-learning framework for enhancing cross-lingual natural language inference by constructing cloze-style questions through cross-linguistic templates. |
| Outcome: | The proposed framework significantly outperforms existing models under cross-lingual transfer settings. |
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)
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| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
| Approach: | They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance . |
| Outcome: | The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories. |
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. |
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization (2025.acl-long)
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| Challenge: | Large language models generate unintended outputs due to their unsupervised nature. |
| Approach: | They propose a method to construct preference pairs of selected and rejected LLMs by repeated random sampling to improve alignment performance. |
| Outcome: | The proposed method improves performance as the sample size increases. |
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection (2023.findings-emnlp)
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| Challenge: | Existing methods for data-driven annotations require domain-specific and task-aligned supervision. |
| Approach: | They propose a multi-label and multi-target sampling strategy to optimize the annotation quality. |
| Outcome: | The proposed method significantly improves performance and learning efficacy on the benchmark stance detection corpora. |
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. |
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. |
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)
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| Challenge: | Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data. |
| Approach: | They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data. |
| Outcome: | The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets. |