Papers by Zhongyang Liu
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)
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| Challenge: | Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift. |
| Approach: | They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks. |
| Outcome: | The proposed framework outperforms baselines on Chinese and English CCR datasets. |
Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction (2026.findings-acl)
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| Challenge: | Most venture capital investments fail, while a few deliver outsized returns. |
| Approach: | They propose a framework that synthesizes relational evidence across sources . they propose combining information-gain-driven retriever and knowledge base to ground reasoning . |
| Outcome: | The proposed framework achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines. |
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (D19-1)
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| Challenge: | Understanding event and event-centered commonsense reasoning is crucial for natural language processing (NLP). |
| Approach: | They propose a If-Then commonsense reasoning dataset Atomic and an RNN-based Seq2Seq model to facilitate this. |
| Outcome: | The proposed model improves the accuracy and diversity of inferences compared with baseline methods. |
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training (C18-1)
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| Challenge: | Story generation is a challenging problem in artificial intelligence (AI) . previous work focused on learning statistical models of event sequences from large-scale text corpora . |
| Approach: | They propose to use adversarial training to generate reasonable story endings . their model includes a generator that defines the policy of generating a story ending . |
| Outcome: | The proposed model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods. |