COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks

TL;DR

COOL introduces a novel outlook attention mechanism for transformer models, enhancing local context encoding in NLP tasks like question answering, and demonstrates competitive performance improvements over baseline models.

Contribution

The paper proposes COOL, a new outlook attention mechanism that improves local context modeling in transformer-based NLP models, inspired by vision outlooker techniques.

Findings

COOL improves performance on NLP tasks compared to baseline models.

The approach achieves competitive results with state-of-the-art methods.

Empirical evaluation confirms the effectiveness of COOL in various NLP tasks.

Abstract

Vision outlooker improves the performance of vision transformers, which implements a self-attention mechanism by adding an outlook attention, a form of local attention. In natural language processing, as has been the case in computer vision and other domains, transformer-based models constitute the state-of-the-art for most processing tasks. In this domain, too, many authors have argued and demonstrated the importance of local context. We present an outlook attention mechanism, COOL, for natural language processing. COOL, added on top of the self-attention layers of a transformer-based model, encodes local syntactic context considering word proximity and more pair-wise constraints than dynamic convolution used by existing approaches. A comparative empirical performance evaluation of an implementation of COOL with different transformer-based models confirms the opportunity for improvement over a baseline using the original models alone for various natural language processing tasks, including question answering. The proposed approach achieves competitive performance with existing state-of-the-art methods on some tasks.