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Answer Generation through Unified Memories over Multiple Passages
Machine reading comprehension methods that generate answers by referring to multiple passages for a question have gained much attention in AI and NLP communities.
Conclusion-Supplement Answer Generation for Non-Factoid Questions
This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion.
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