Papers by Yunshan Ma
MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits (2026.acl-long)
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| Challenge: | Document Question Answering (DQA) requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. |
| Approach: | They propose a multi-armed bandit-based DQA framework that explicitly models the varying importance of multiple implicit aspects in a query. |
| Outcome: | The proposed framework shows an improvement of 5%-18% over the state-of-the-art method on four benchmarks. |
Dialogue State Tracking with Incremental Reasoning (2021.tacl-1)
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| Challenge: | Empirical results show that our method outperforms the state-of-the-art methods in terms of joint belief accuracy. |
| Approach: | They propose to track dialogue states gradually with reasoning over dialogue turns using the back-end data. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods in terms of joint belief accuracy for a large-scale human–human dialogue dataset. |
Scattered Hypothesis Generation for Open-Ended Event Forecasting (2026.findings-acl)
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| Challenge: | Existing methods for event forecasting focus on the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. |
| Approach: | They propose a reinforcement learning framework that optimizes inclusiveness and diversity of the hypothesis by integrating validity-gated score into the overall objective. |
| Outcome: | The proposed framework outperforms baselines on two real-world benchmark datasets. |
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding (2024.acl-long)
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| Challenge: | Existing research in complex event analysis has made significant strides but is constrained by inadequate natural language processing techniques. |
| Approach: | They propose a novel approach using Large Language Models to extract and analyze the event chain within TCE, characterized by their key points and timestamps. |
| Outcome: | The proposed model performs comparable to models with long context window and retrieval-augmented generation method in three distinct tasks . |