Lexically Grounded Subword Segmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492880" target="_blank" >RIV/00216208:11320/24:10492880 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2024.emnlp-main.421/" target="_blank" >https://aclanthology.org/2024.emnlp-main.421/</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Lexically Grounded Subword Segmentation

Original language description

We present three innovations in tokenization and subword segmentation. First, we propose to use unsupervised morphological analysis with Morfessor as pre-tokenization. Second, we present an algebraic method for obtaining subword embeddings grounded in a word embedding space. Based on that, we design a novel subword segmentation algorithm that uses the embeddings, ensuring that the procedure considers lexical meaning. Third, we introduce an efficient segmentation algorithm based on a subword bigram model that can be initialized with the lexically aware segmentation method to avoid using Morfessor and large embedding tables at inference time. We evaluate the proposed approaches using two intrinsic metrics and measure their performance on two downstream tasks: part-of-speech tagging and machine translation. Our experiments show significant improvements in the morphological plausibility of the segmentation when evaluated using segmentation precision on morpheme boundaries and improved Rényi efficiency in

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP)

ISBN

979-8-89176-164-3

ISSN

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e-ISSN

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Number of pages

18

Pages from-to

7403-7420

Publisher name

Association for Computational Linguistics

Place of publication

Kerrville, TX, USA

Event location

Miami, FL, USA

Event date

Nov 12, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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