Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer

• URL: http://arxiv.org/abs/2402.02926v1
• Date: Mon, 5 Feb 2024 11:47:36 GMT
• Title: Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer
• Authors: V.S.D.S.Mahesh Akavarapu and Arnab Bhattacharya
• Abstract summary: Identification of cognates across related languages is one of the primary problems in historical linguistics. We present a transformer-based architecture inspired by computational biology for the task of automated cognate detection.
• Score: 4.609569810881602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods for cognate identification are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision, thereby proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance.

Related papers

• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  Formal language theory pertains specifically to recognizers. It is common to instead use proxy tasks that are similar in only an informal sense. We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages [1.7622337807395716]
  Exploiting cognates for transfer learning in under-resourced languages is an exciting opportunity for language understanding tasks. Previous approaches mainly focused on supervised cognate detection tasks based on orthographic, phonetic or state-of-the-art contextual language models. This paper proposes a novel language-agnostic weakly-supervised deep cognate detection framework for under-resourced languages.
  arXiv  Detail & Related papers  (2023-11-09T05:46:41Z)
• Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction [4.609569810881602]
  We adapt MSA Transformer, a protein language model, to the problem of automated phonological reconstruction. MSA Transformer trains on multiple sequence alignments as input and is, thus, apt for application on aligned cognate words. We also apply the model on another associated task, namely, cognate reflex prediction, where a reflex word in a daughter language is predicted based on cognate words from other daughter languages.
  arXiv  Detail & Related papers  (2023-10-11T13:34:22Z)
• Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning [80.08139343603956]
  In cross-lingual named entity recognition, self-training is commonly used to bridge the linguistic gap. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling.
  arXiv  Detail & Related papers  (2023-05-23T02:52:16Z)
• Trimming Phonetic Alignments Improves the Inference of Sound Correspondence Patterns from Multilingual Wordlists [3.096615629099617]
  Methods for the automatic inference of correspondence patterns from phonetically aligned cognate sets have been proposed. Since annotation is tedious and time consuming, it would be desirable to find ways to improve aligned cognate data automatically. We propose a workflow that trims phonetic alignments in comparative linguistics prior to the inference of correspondence patterns.
  arXiv  Detail & Related papers  (2023-03-31T09:55:48Z)
• Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers [86.64972552583941]
  We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
  arXiv  Detail & Related papers  (2022-05-23T16:47:37Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• Utilizing Wordnets for Cognate Detection among Indian Languages [50.83320088758705]
  We detect cognate word pairs among ten Indian languages with Hindi. We use deep learning methodologies to predict whether a word pair is cognate or not. We report improved performance of up to 26%.
  arXiv  Detail & Related papers  (2021-12-30T16:46:28Z)
• Generative latent neural models for automatic word alignment [0.0]
  Variational autoencoders have been recently used in various of natural language processing to learn in an unsupervised way latent representations that are useful for language generation tasks. In this paper, we study these models for the task of word alignment and propose and assess several evolutions of a vanilla variational autoencoders. We demonstrate that these techniques can yield competitive results as compared to Giza++ and to a strong neural network alignment system for two language pairs.
  arXiv  Detail & Related papers  (2020-09-28T07:54:09Z)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.