Papers by Jianshu Chen
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on the confidence calibration of LLMs have not explored the effects of different prompting strategies on LLM performance. |
| Approach: | They propose Fact-and-Reflection prompting which improves LLM confidence calibration . they propose to use human cognition to elicit known "facts" and ask model to "reflect" over them . |
| Outcome: | The proposed method lowers the expected calibration error by 23.5% on multi-purpose QA tasks. |
How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method (2023.eacl-main)
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| Challenge: | Existing studies on sentence representation learning focus on human annotation, but they neglect the critical property that essential contents should contribute to sentence semantics more than non-essential contents when encoding a sentence. |
| Approach: | They propose a perturbation method for unsupervised semantic analysis that uses a sentence compression metric to adapt sentence compression datasets for automatic evaluation. |
| Outcome: | The proposed method can capture the main semantics of sentences better than several SOTA unsupervised sentence embedding models. |
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)
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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
| Challenge: | MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models . |
| Approach: | They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. |
| Outcome: | The proposed model can significantly compress a large model without significant performance drop. |
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)
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| Challenge: | Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text. |
| Approach: | They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation. |
| Outcome: | The proposed model improves the performance of existing language models across a diverse set of language tasks. |
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention (P19-1)
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| Challenge: | Existing models for limited-domain RNNs are difficult to scale due to the complexity of the inputs. |
| Approach: | They propose to use dialog acts to build a multi-layer hierarchical graph with a disentangled self-attention network. |
| Outcome: | The proposed model improves on the baselines on automatic and human evaluation metrics. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks (2023.findings-acl)
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| Challenge: | Existing semi-parametric language models lack the capacity to perform zero-shot tasks . large language models have shown impressive zero-shoot ability, but huge model size costs . semi-parametric language model can be used to augment a smaller language model with retrieved background knowledge . |
| Approach: | They propose a semi-parametric language model for zero-shot task generalization that augments a smaller language model with retrieved related background knowledge. |
| Outcome: | The proposed model outperforms T0-3B by 16% across seven diverse evaluation tasks while being 3.8x smaller in scale. |
Improving Question Answering with External Knowledge (D19-58)
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| Challenge: | ARC-Easy, ARC Challenge, and OpenBookQA use Wikipedia to augment training data . performance degrades when additional instances exhibit higher difficulty than original training data. |
| Approach: | They propose two methods for exploiting external knowledge for QA in science . they enrich the original corpus with relevant text snippets from an open-domain resource . the second method simply increases the amount of training data by appending additional in-domain instances. |
| Outcome: | The proposed methods achieve gains in accuracy of 8.1%, 13.0%, and 12.8% on science QA tasks. |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
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Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
| Approach: | They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals. |
| Outcome: | The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. |
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)
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| Challenge: | Existing models for dialogue comprehension are not available for the pre-training of such a model. |
| Approach: | They propose a narrative-guided pre-training strategy that learns by narrating key information from a dialogue input. |
| Outcome: | The proposed model performs better on four dialogue-based tasks and is comparable to existing models. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation (2024.emnlp-main)
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KaShun Shum, Minrui Xu, Jianshu Zhang, Zixin Chen, Shizhe Diao, Hanze Dong, Jipeng Zhang, Muhammad Raza
| Challenge: | Experimental results show that a well-calibrated model is more reliable than a fine-tuned model due to “tuning-induced mis-calibration”. |
| Approach: | They propose a method which utilizes a small portion of teacher’s knowledge to obtain a reliable language model in a cost-efficient way. |
| Outcome: | The proposed method reduces the computational burden by utilizing teacher's knowledge to obtain a reliable language model in a cost-efficient way. |
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT (2020.acl-main)
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| Challenge: | Zero pronoun recovery and resolution aim at recovering the dropped pronounce and pointing out its anaphoric mentions. |
| Approach: | They propose to solve two tasks together to recover the dropped pronoun and point out its anaphoric mentions. |
| Outcome: | The proposed model outperforms previous state of the arts benchmarks on two benchmarks. |
Skills-in-Context: Unlocking Compositionality in Large Language Models (2024.findings-emnlp)
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| Challenge: | eliciting compositional generalization capabilities in large language models is challenging for advanced LLMs because they lack foundational skills and compositional examples in the same prompt context. |
| Approach: | They propose to use compositional generalization capabilities in large language models to elicit compositional skills in a prompt context. |
| Outcome: | The proposed structure enables LLMs to tackle more challenging problems with as few as two exemplars and unlocks their latent potential. |
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024.naacl-long)
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Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
| Challenge: | Existing large language models have limited ability to perform tasks effectively. |
| Approach: | They propose a large-scale multimodal chart instruction dataset with 600k instances supporting diverse tasks and chart types. |
| Outcome: | The proposed LMM achieves state-of-the-art performance on existing chart QA benchmarks. |
Logical Natural Language Generation from Open-Domain Tables (2020.acl-main)
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| Challenge: | Existing studies on neural natural language generation focus on surface-level realizations with limited emphasis on logical inference. |
| Approach: | They propose a task where a model is tasked with generating natural language statements that can be logically entailed by facts in an open-domain semi-structured table. |
| Outcome: | The proposed task is based on the existing TabFact dataset with a wide range of logical/symbolic inferences. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
CEO: Corpus-based Open-Domain Event Ontology Induction (2024.findings-eacl)
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| Challenge: | Existing event-centric NLP models restrict their generalization capabilities by limiting the pre-defined ontology. |
| Approach: | They propose a Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined ontologies. |
| Outcome: | The proposed model can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making it more trustworthy and easier to be further curated. |
EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning (2026.acl-long)
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| Challenge: | Existing semantic similarity methods struggle to accurately identify semantically equivalent steps in domain-specific contexts like mathematical reasoning. |
| Approach: | They propose a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search. |
| Outcome: | The proposed approach reduces token consumption while improving reasoning efficiency and accuracy on Qwen2.5-Math-7B-Instruct on GSM8K. |
PIVOINE: Instruction Tuning for Open-world Entity Profiling (2023.findings-emnlp)
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| Challenge: | Existing methods for information extraction focus on a closed-world setting, but PIVOINE is a promising solution to tackle the open-world problem of entity profiling. |
| Approach: | They propose to develop an LLM that performs Open-world Entity Profiling with instruction tuning to extract desirable entity profiles . they construct INSTRUCTOPENWIKI, a substantial instruction-tuning dataset for Open-World Entity Profiles . |
| Outcome: | The proposed model outperforms existing methods and ChatGPT-based baselines on unseen and out-of-ontology cases. |
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)
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| Challenge: | Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well. |
| Approach: | They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data. |
| Outcome: | The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3. |