1 code implementation • ICLR 2022 • Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.
no code implementations • 14 Jan 2022 • Yong Shan, Jinchao Zhang, Zekang Li, Yang Feng, Jie zhou
Previous researches on dialogue system assessment usually focus on the quality evaluation (e. g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics.
no code implementations • 24 Apr 2021 • Yong Shan, Yang Feng, Chenze Shao
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously.
no code implementations • ACL 2020 • Yong Shan, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Cheng Niu, Jie zhou
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs.
Ranked #6 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
no code implementations • 5 Nov 2019 • Yong Shan, Yang Feng, Jinchao Zhang, Fandong Meng, Wen Zhang
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.