82 citations · 432 across the 55 of their papers we have counts for
6 papers · 1 filter
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network
Yuqi Ren, Deyi Xiong
Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridg…
Secoco: Self-Correcting Encoding for Neural Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +3
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting pred…
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
Yuqi Ren, Deyi Xiong
Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly conc…
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +2
Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying result…
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
Jinsong Su, Jialong Tang, Hui Jiang +6
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. Ho…
Integrating Pre-trained Model into Rule-based Dialogue Management
Jun Quan, Meng Yang, Qiang Gan +9
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to m…