Domain-Weighted Batch Sampling for Neural Dependency Parsing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A3RG8BT5L" target="_blank" >RIV/00216208:11320/25:3RG8BT5L - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2024.mwe-1.24" target="_blank" >https://aclanthology.org/2024.mwe-1.24</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Domain-Weighted Batch Sampling for Neural Dependency Parsing

Original language description

In neural dependency parsing, as well as in the broader field of NLP, domain adaptation remains a challenging problem. When adapting a parser to a target domain, there is a fundamental tension between the need to make use of out-of-domain data and the need to ensure that syntactic characteristic of the target domain are learned. In this work we explore a way to balance these two competing concerns, namely using domain-weighted batch sampling, which allows us to use all available training data, while controlling the probability of sampling in- and out-of-domain data when constructing training batches. We conduct experiments using ten natural language domains and find that domain-weighted batch sampling yields substantial performance improvements in all ten domains compared to a baseline of conventional randomized batch sampling.

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

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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 Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024

ISBN

978-2-493-81420-3

ISSN

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e-ISSN

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Number of pages

9

Pages from-to

198-206

Publisher name

ELRA and ICCL

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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