Papers by Zeyu Dai
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
| Approach: | They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations. |
| Outcome: | The proposed approach outperforms previous systems on the benchmark dataset PDTB for discourse parsing. |
Weakly Supervised Subevent Knowledge Acquisition (2020.emnlp-main)
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| Challenge: | Subevents elaborate an event and exist in event descriptions. |
| Approach: | They propose a weakly supervised approach to extract subevent relation tuples from text . they then use the initial seed subeven pairs to train a contextual classifier . |
| Outcome: | The proposed method is high quality and covers a wide range of event types. |
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents (2025.findings-acl)
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| Challenge: | Recent studies have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. |
| Approach: | They propose a dataset and benchmark to evaluate the memory capability of LLM-based agents from multiple aspects including their effectiveness, efficiency, and capacity. |
| Outcome: | The proposed benchmark incorporates factual memory and reflective memory as different levels, and proposes participation and observation as various interactive scenarios. |
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents (2026.findings-acl)
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| Challenge: | Existing plans for large language model-based agents are limited by their granularity and lack flexibility. |
| Approach: | They propose a self-adaptive hierarchical planning mechanism that mimics human planning strategies and generates self-adapted hierarchic plans tailored to the varying difficulty levels of different tasks. |
| Outcome: | The proposed method significantly improves task execution success rates while mitigating overthinking at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks. |
A Joint Model for Structure-based News Genre Classification with Application to Text Summarization (2021.findings-acl)
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| Challenge: | Existing models for structure-based news genre classification identify news structure types and news elements . authors show that the model outperforms variants that perform two tasks independently . |
| Approach: | They propose a joint model that identifies one of four commonly used news structures for a news article and recognizes a sequence of news elements within the article that define the corresponding news structure. |
| Outcome: | The proposed model outperforms variants that perform two tasks independently . it predicts news structure type and news elements and improves text summarization . |
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)
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| Challenge: | Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction. |
| Approach: | They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence. |
| Outcome: | The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB. |
Building Context-aware Clause Representations for Situation Entity Type Classification (D18-1)
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| Challenge: | Existing models for categorizing clauses based on situation entity types do not provide accurate results. |
| Approach: | They propose to build context-aware clause representations for predicting situation entity types of clauses by modeling context influences and inter-dependencies of clause. |
| Outcome: | The proposed model achieves state-of-the-art performance on genre-rich dataset MASC+Wiki . |