Papers by Zhihan Zhou
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)
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| Challenge: | Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio. |
| Approach: | They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. |
| Outcome: | The proposed strategy outperforms baselines in winning rate, excess returns over the market, and the average return on each transaction. |
Learning Dialogue Representations from Consecutive Utterances (2022.naacl-main)
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| Challenge: | Dialogue Sentence Embedding (DSE) is a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue-oriented tasks. |
| Approach: | They propose a self-supervised contrastive learning method that learns dialogue representations suitable for a wide range of dialogue tasks. |
| Outcome: | The proposed method outperforms baselines on five dialogue tasks on a few-shot and zero-shot datasets. |
POP-CEE: Position-oriented Prompt-tuning Model for Causal Emotion Entailment (2024.findings-acl)
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| Challenge: | Existing methods for emotion analysis in conversations ignore the specific semantic associations between emotions and cause utterances. |
| Approach: | They propose a position-oriented prompt-tuning model to solve the CEE task in an end-to-end manner. |
| Outcome: | The proposed model achieves state-of-the-art performance on a benchmark dataset. |
EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)
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| Challenge: | Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability. |
| Approach: | They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base. |
| Outcome: | The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents. |
ACSE: An Ancient Character Semantic-Aware Embedding for Large Language Models (2026.findings-acl)
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| Challenge: | Existing studies on pre-Qin documents are insufficient to understand ancient characters . ancient characters have a low level of digitization and training corpora are extremely scarce . |
| Approach: | They propose a semantic-aware embedding for ancient Chinese characters that integrates glyphs and lexicality into modern Chinese semantic space. |
| Outcome: | The proposed model integrates glyph and lexicality of ancient characters and maps them to the modern Chinese semantic space. |
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020.acl-main)
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| Challenge: | Existing few-shot learning methods for slot tagging are based on similarity-based methods, but they are difficult to apply to an unseen domain due to the discrepancy of label sets. |
| Approach: | They propose a label-enhanced task-adaptive projection network to transfer abstract label dependency patterns as transition scores into the conditional random field (CRF) Experimental results show that their model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |
| Outcome: | The proposed model outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |