Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention

• URL: http://arxiv.org/abs/2010.07891v2
• Date: Tue, 27 Oct 2020 16:16:18 GMT
• Title: Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
• Authors: Ekta Sood, Simon Tannert, Philipp Mueller, Andreas Bulling
• Abstract summary: A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms. We propose a novel hybrid text saliency model that combines a cognitive model of reading with explicit human gaze supervision. Our work introduces a practical approach for bridging between data-driven and cognitive models and demonstrates a new way to integrate human gaze-guided neural attention into NLP tasks.
• Score: 14.940723222424749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural language processing(NLP). We propose a novel hybrid text saliency model(TSM) that, for the first time, combines a cognitive model of reading with explicit human gaze supervision in a single machine learning framework. On four different corpora we demonstrate that our hybrid TSM duration predictions are highly correlated with human gaze ground truth. We further propose a novel joint modeling approach to integrate TSM predictions into the attention layer of a network designed for a specific upstream NLP task without the need for any task-specific human gaze data. We demonstrate that our joint model outperforms the state of the art in paraphrase generation on the Quora Question Pairs corpus by more than 10% in BLEU-4 and achieves state of the art performance for sentence compression on the challenging Google Sentence Compression corpus. As such, our work introduces a practical approach for bridging between data-driven and cognitive models and demonstrates a new way to integrate human gaze-guided neural attention into NLP tasks.

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