Papers by Chenchen Ye
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two 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 . |
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)
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| Challenge: | Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes. |
| Approach: | They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods. |
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)
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| Challenge: | Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths. |
| Approach: | They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths . |
| Outcome: | The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |