# Cognition-aware Cognate Detection

arXiv: https://arxiv.org/abs/2112.08087 · HTML: https://mlsanity.com/paper/2112.08087/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages](https://mlsanity.com/paper/2005.00085/) (2020)
- [Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://mlsanity.com/paper/1902.10068/) (2019)
- [Improving sentence compression by learning to predict gaze](https://mlsanity.com/paper/1604.03357/) (2016)
- [Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?](https://mlsanity.com/paper/1804.05416/) (2018)
- [Improving Cross-Lingual Word Embeddings by Meeting in the Middle](https://mlsanity.com/paper/1808.08780/) (2018)
- [Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour](https://mlsanity.com/paper/2005.12078/) (2020)
- [Harnessing Cognitive Features for Sarcasm Detection](https://mlsanity.com/paper/1701.05574/) (2017)
- [Classifying Referential and Non-referential It Using Gaze](https://mlsanity.com/paper/2006.13327/) (2020)

## Built on by (most cited first, 3 of 3)

- [Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer](https://mlsanity.com/paper/2402.02926/) (2024)
- [Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages](https://mlsanity.com/paper/2311.05155/) (2023)
- [When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates](https://mlsanity.com/paper/2606.13218/) (2026)

## Abstract (arXiv)

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.