Papers by Woon Sang Cho
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models (D19-56)
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| Challenge: | Ambiguous user queries can result in multiple topics being retrieved from search engines. |
| Approach: | They propose a task of generating a common question from multiple documents by training an RNN-based single encoder-decoder generator from document pairs and then a model that aggregates these word distributions to generate a question. |
| Outcome: | The proposed model significantly outperforms existing models when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset. |
Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |