Papers by Shaohua Yang
Commonsense Justification for Action Explanation (D18-1)
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| Challenge: | a recent study examines the commonsense reasoning used by humans to justify an AI prediction. |
| Approach: | They propose an approach that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. |
| Outcome: | The proposed model achieves significantly higher performance in both action prediction and justification. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |
What Action Causes This? Towards Naive Physical Action-Effect Prediction (P18-1)
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| Challenge: | a new task on naive physical action-effect prediction addresses the relationship between concrete actions and their effects on the state of the physical world as depicted by images. |
| Approach: | They propose a task that harnesses web image data to facilitate action-effect prediction. |
| Outcome: | The proposed approach harnesses web image data through distant supervision to facilitate learning for action-effect prediction. |