Papers by Kun Qian
Domain Adaptive Dialog Generation via Meta Learning (P19-1)
Copied to clipboard
| Challenge: | Existing methods to adapt dialog data to new domains with limited resources are expensive . a domain adaptive dialog system model learns from multiple rich-resource tasks and adapts to new tasks with minimal training samples. |
| Approach: | They propose a domain adaptive dialog generation method based on meta-learning . they train a dialog system model using multiple rich-resource single-domain dialog data . |
| Outcome: | The proposed method can learn a competitive dialog system on a new domain with minimal training examples. |
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
Copied to clipboard
Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (2023.emnlp-demo)
Copied to clipboard
Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li
| Challenge: | Existing large language models (LLMs) have a tendency to hallucinate and provide creative and fluent responses that are not factually accurate. |
| Approach: | They propose a tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors. |
| Outcome: | The proposed tool detects errors in text and evaluates their factuality and suggests revisions based on the collected evidence. |
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Task-oriented dialogs (TOD) require a model to generate a response that optimizes for task-related metrics. |
| Approach: | They propose a faster generation procedure that samples from independent next-word distributions and introduce a fine-grained reward function to help the model focus on learning key information in a dialog. |
| Outcome: | The proposed algorithm achieves state-of-the-art performance on an offline task with 15% training time reduction compared to a standard RL algorithm using auto-regressive generation. |
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. |
| Approach: | They propose a framework that combines active learning and weak supervision to solve this problem. |
| Outcome: | The proposed framework enables learning of high-quality models from a dozen labeled examples. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
Copied to clipboard
Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
Database Search Results Disambiguation for Task-Oriented Dialog Systems (2022.naacl-main)
Copied to clipboard
Kun Qian, Satwik Kottur, Ahmad Beirami, Shahin Shayandeh, Paul Crook, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar
| Challenge: | Task-oriented dialog systems can't handle multiplesearch results when querying a database due to the lack of such scenarios in existing datasets. |
| Approach: | They propose a task that focuses on disambiguating database search results by synthetically generating turns through a pre-defined grammar and collecting human paraphrases for a subset. |
| Outcome: | The proposed task improves performance on DSR-disambiguation even in the absence of in-domain data, suggesting it can be learned as a universal dialog skill. |
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)
Copied to clipboard
Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say . |
| Approach: | They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset . |
| Outcome: | a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data . |
DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites . |
| Approach: | They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences. |
| Outcome: | The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites . |
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
Copied to clipboard
Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
How to Build User Simulators to Train RL-based Dialog Systems (D19-1)
Copied to clipboard
| Challenge: | RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive. |
| Approach: | They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators. |
| Outcome: | The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly. |
Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)
Copied to clipboard
| Challenge: | Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference. |
| Approach: | They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information. |
| Outcome: | The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference. |
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts (2025.naacl-short)
Copied to clipboard
| Challenge: | Training conversational question-answering systems requires in-domain data, which is often scarce in practice. |
| Approach: | They propose a bottom-up approach where QA pairs are generated first and combined into a coherent dialogue. |
| Outcome: | The proposed approach produces more realistic and higher-quality dialogues compared to top-down methods. |
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking (2023.findings-eacl)
Copied to clipboard
| Challenge: | Prompt-based methods with large pre-trained language models have shown impressive unaided performance across many NLP tasks. |
| Approach: | They propose a meta-learning scheme to stabilize the ability of the model to perform well under various prompts and introduce a saliency model to limit dialogue text length. |
| Outcome: | The proposed model improves on large pre-trained language models with labeled in-context exemplars and can be used to generate more exemplar queries. |
Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity . |
| Approach: | They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals. |
| Outcome: | The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
Copied to clipboard
| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Low-resource Deep Entity Resolution with Transfer and Active Learning (P19-1)
Copied to clipboard
| Challenge: | Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. |
| Approach: | They propose a deep learning-based method that targets low-resource settings for ER by combining transfer learning and active learning. |
| Outcome: | The proposed method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels. |
Exploiting Structure in Representation of Named Entities using Active Learning (C18-1)
Copied to clipboard
| Challenge: | Named entities are atomic objects of reference and reasoning in many knowledge-centric applications. |
| Approach: | They propose an active-learning based framework that drastically reduces the labeled data required to learn entities' structures. |
| Outcome: | The proposed framework outperforms handwritten programs and supervised learning models in relation extraction and entity resolution tasks. |
VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent benchmarks release only training and validation sets, keeping the test set labels closed-source. |
| Approach: | They propose to extract variables from each test case and define a value range for each variable. |
| Outcome: | The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks. |