Cognition-aware Cognate Detection

TL;DR

This paper introduces a novel cognate detection method that incorporates cognitive features derived from human gaze data, significantly improving detection accuracy and enabling scalable cognitive feature prediction.

Contribution

It presents a new approach using gaze-based cognitive features for cognate detection and demonstrates their effectiveness with both collected and predicted data.

Findings

10% improvement with gaze features

12% improvement with predicted gaze features

Gaze data and models are publicly released

Abstract

Automatic detection of cognates helps downstream NLP tasks of Machine Translation, Cross-lingual Information Retrieval, Computational Phylogenetics and Cross-lingual Named Entity Recognition. Previous approaches for the task of cognate detection use orthographic, phonetic and semantic similarity based features sets. In this paper, we propose a novel method for enriching the feature sets, with cognitive features extracted from human readers' gaze behaviour. We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection. However, gaze data collection and annotation is a costly task. We use the collected gaze behaviour data to predict cognitive features for a larger sample and show that predicted cognitive features, also, significantly improve the task performance. We report improvements of 10% with the collected gaze features, and 12% using the predicted gaze features, over the previously proposed approaches. Furthermore, we release the collected gaze behaviour data along with our code and cross-lingual models.