Papers by WenHan Chao
TP-Detector: Detecting Turning Points in the Engineering Process of Large-scale Projects (2023.emnlp-demo)
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| Challenge: | Extensive experiments demonstrate the effectiveness of our proposed method on a constructed dataset compared to baseline methods. |
| Approach: | They propose a novel task of detecting turning points in the engineering process of large-scale projects by treating news streams as a window with multiple instances. |
| Outcome: | The proposed mode is able to detect transitions in news streams with multiple instances. |
Argumentation-Driven Evidence Association in Criminal Cases (2021.findings-emnlp)
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| Challenge: | Existing evidence association methods focus on extracting rich semantic representation and then calculate cosine distance between text representations. |
| Approach: | They propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step. |
| Outcome: | The proposed method can be used to calculate distance between evidence pairs on a real-world dataset. |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |
Interpretable Rationale Augmented Charge Prediction System (C18-2)
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| Challenge: | Existing studies treat charge prediction as a text classification problem, but in the field of justice, every decision may be a matter of life and death. |
| Approach: | They propose to extract readable rationales from text and then create a rationale augmented classification model to enhance the prediction accuracy. |
| Outcome: | The proposed system can extract readable rationales in a high consistency with manual annotation and is comparable with the attention model in prediction accuracy. |
CRST: a Claim Retrieval System in Twitter (C18-2)
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| Challenge: | CRST retrieves tweets containing arguments for controversial topics from Twitter. |
| Approach: | They propose a system that retrieves tweets containing claims for a given topic from Twitter. |
| Outcome: | The proposed system outperforms existing claims retrieval and argument mining systems. |