Negative Lexical Constraints in Neural Machine Translation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10476122" target="_blank" >RIV/00216208:11320/23:10476122 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Negative Lexical Constraints in Neural Machine Translation

Original language description

This paper explores negative lexical constraining in English to Czech neural machine translation. Negative lexical constraining is used to prohibit certain words or expressions in the translation produced by the neural translation model. We compared various methods based on modifying either the decoding process or the training data. The comparison was performed on two tasks: paraphrasing and feedback-based translation refinement. We also studied to which extent these methods &quot;evade&quot; the constraints presented to the model (usually in the dictionary form) by generating a different surface form of a given constraint.We propose a way to mitigate the issue through training with stemmed negative constraints to counter the model&apos;s ability to induce a variety of the surface forms of a word that can result in bypassing the constraint. We demonstrate that our method improves the constraining, although the problem still persists in many cases.

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Article name in the collection

Proceedings of Machine Translation Summit XIX vol. 1: Research Track

ISBN

978-4-9913461-0-1

ISSN

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e-ISSN

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Number of pages

13

Pages from-to

372-384

Publisher name

Asia-Pacific Association for Machine Translation (AAMT)

Place of publication

Kyoto, Japan

Event location

Macau SAR, China

Event date

Sep 4, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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