CLApr 12, 2016
Improving sentence compression by learning to predict gaze
arXiv:1604.03357v1130 citations
Originality Synthesis-oriented
AI Analysis
This addresses sentence compression for NLP applications, but it is incremental as it builds on existing methods with new data.
The paper tackled sentence compression by using eye-tracking data to train models, achieving performance competitive with or better than state-of-the-art approaches.
We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.