论文标题
通过人类凝视引导的神经关注改善自然语言处理任务
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
论文作者
论文摘要
到目前为止，缺乏语料库在将人类凝视数据整合为自然语言处理（NLP）神经注意机制中的监督信号方面的进步有限。我们提出了一种新型的混合文本显着性模型（TSM），该模型首次将阅读的认知模型与明确的人类目光监督结合在单个机器学习框架中。在四个不同的语料库中，我们证明了混合TSM持续时间预测与人类凝视地面真理高度相关。我们进一步提出了一种新型的联合建模方法，将TSM预测整合到专为特定上游NLP任务的网络的注意力层中，而无需任何特定于任务的人类凝视数据。我们证明，我们的联合模型在Quora问题配对上的释义生成中的最高现状超过了BLEU-4的10％以上，并且在具有挑战性的Google句子压缩语料库中实现了句子压缩的最新表现。因此，我们的工作引入了一种实用方法，用于在数据驱动和认知模型之间进行桥接，并展示了一种将人类凝视引导的神经关注整合到NLP任务中的新方法。
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.
