Cognition-aware Cognate Detection
Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak, Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Speech and dialogue systems · Natural Language Processing Techniques
