Wan Wei


A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension
Jiahua Liu | Wan Wei | Maosong Sun | Hao Chen | Yantao Du | Dekang Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.


pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection
Wan Wei | Xiao Zhang | Xuqin Liu | Wei Chen | Tengjiao Wang
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)