Why Do Neural Response Generation Models Prefer Universal Replies?

ICLR 2019 Bowen WuNan JiangZhifeng GaoMengyuan LiZongsheng WangSuke LiQihang FengWenge RongBaoxun Wang

Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are prone to producing short and generic replies, making it infeasible to tackle open-domain challenges... (read more)

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