Papers by Yinggong Zhao
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
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| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Explicit Span-Sentence Predication solves location unit ambiguity problem in many languages, allowing model to determine which sentence contains the answer span when sentence itself has not been clearly defined at all. |
| Approach: | They propose a machine-learning reader with Explicit Span-Sentence Predication to solve this problem by analyzing Chinese sentences. |
| Outcome: | The proposed reader achieves state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages. |
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks (2020.emnlp-main)
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| Challenge: | Neural networks typically need large labeled data for training and are not easily interpretable. |
| Approach: | They propose a type of recurrent neural networks that combine neural networks and regular expression rules. |
| Outcome: | The proposed recurrent neural networks outperform previous neural approaches in low- and zero-shot scenarios and remain very competitive in rich-resource settings. |
Learning Numeral Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embedding methods do not learn numeral embedds well because numerals are limited in number and their appearances in training corpora are highly scarce. |
| Approach: | They propose two numeral embedding methods that can handle the out-of-vocabulary problem for numerals. |
| Outcome: | The proposed methods can handle the out-of-vocabulary problem for numerals. |
Dialogue State Tracking with Explicit Slot Connection Modeling (2020.acl-main)
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| Challenge: | Existing methods to track dialogue state are lacking in multi-domain scenarios. |
| Approach: | They propose a model that explicitly considers slot correlations across domains . they propose ellipsis and reference to express values that have been mentioned by slots from other domains. |
| Outcome: | The proposed model outperforms existing models on multi-domain datasets and achieves state-of-the-art performance. |