5 citations · 14 across the 8 of their papers we have counts for
6 papers · 1 filter
TVShowGuess: Character Comprehension in Stories as Speaker Guessing
Yisi Sang, Xiangyang Mou, Mo Yu +3
We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes…
Efficient Long Sequence Encoding via Synchronization
Xiangyang Mou, Mo Yu, Bingsheng Yao +1
Pre-trained Transformer models have achieved successes in a wide range of NLP tasks, but are inefficient when dealing with long input sequences. Existing studies try to overcome th…
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Xiangyang Mou, Chenghao Yang, Mo Yu +4
Recent advancements in open-domain question answering (ODQA), i.e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datase…
Complementary Evidence Identification in Open-Domain Question Answering
Xiangyang Mou, Mo Yu, Shiyu Chang +3
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages tha…
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation
Xiangyang Mou, Brandyn Sigouin, Ian Steenstra +1
Recently, a more challenging state tracking task, Audio-Video Scene-Aware Dialogue (AVSD), is catching an increasing amount of attention among researchers. Different from purely te…
Frustratingly Hard Evidence Retrieval for QA Over Books
Xiangyang Mou, Mo Yu, Bingsheng Yao +4
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We for…