Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405613" target="_blank" >RIV/00216208:11320/19:10405613 - isvavai.cz</a>

Result on the web

<a href="https://www.aclweb.org/anthology/P19-2017.pdf" target="_blank" >https://www.aclweb.org/anthology/P19-2017.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation

Original language description

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling tasks. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the finetuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-toright language model and forcing the model to remember the original left-to-right l

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

<a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

ISBN

978-1-950737-47-5

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

130-135

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Firenze, Italy

Event date

Jul 28, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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