Advancing NLP with Cognitive Language Processing Signals
This work addresses the challenge of enhancing NLP performance across multiple tasks for researchers and practitioners, but it appears incremental as it builds on prior use of such data for single tasks.
The paper tackled the problem of improving NLP tasks by using cognitive language processing data like gaze and EEG features, and found that these methods significantly outperformed baselines on named entity recognition, relation classification, and sentiment analysis.
When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.