Papers by Hanwang Zhang
Hypothetical Training for Robust Machine Reading Comprehension of Tabular Context (2023.findings-acl)
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| Challenge: | Counterfactual training is expensive because of the complexity of tabular data. |
| Approach: | They propose a hypothetical training framework that uses paired examples with different hypothetical questions to supervise the direction of model gradient towards the counterfactual answer change. |
| Outcome: | The proposed framework improves on tabular MRC datasets. |
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
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| Challenge: | Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory . |
| Approach: | They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set. |
| Outcome: | The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns. |
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)
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| Challenge: | Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”. |
| Approach: | They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual. |
| Outcome: | The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions. |
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (2021.emnlp-main)
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| Challenge: | Existing models infer the answer by predicting the sequential relation path or aggregating the hidden graph features. |
| Approach: | They propose a model which jumps between entities at multiple steps . they demonstrate that TransferNet surpasses state-of-the-art models by a large margin . |
| Outcome: | The proposed model surpasses state-of-the-art models on MetaQA and on other datasets. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
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| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| Challenge: | Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale . |
| Approach: | They propose a compositional programming language to represent the reasoning process of complex questions. |
| Outcome: | The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model . |
Empowering Language Understanding with Counterfactual Reasoning (2021.findings-acl)
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| Challenge: | Existing methods for language understanding use the recognized patterns in the testing phase that are inherently different from us humans who have counterfactual thinking. |
| Approach: | They propose a counterfactual Reasoning Model which mimics counterfactive thinking by learning from few counterffact samples. |
| Outcome: | The proposed model can detect and make predictions from textual patterns . it can also detect negative sarcastic puns by comparing them with imaginations . |