Creating Hybrid Dependency Parsers for Syntax-Based MT

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10335587" target="_blank" >RIV/00216208:11320/16:10335587 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/book/10.1007%2F978-3-319-21311-8" target="_blank" >http://link.springer.com/book/10.1007%2F978-3-319-21311-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-21311-8" target="_blank" >10.1007/978-3-319-21311-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Creating Hybrid Dependency Parsers for Syntax-Based MT

Popis výsledku v původním jazyce

Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. In this chapter, we will discuss how different forms and different hybrid combinations of dependency parses effect the overall output of Syntax-Based machine translation both through automatic and manual evaluation. We show results from a variety of individual parsers, including dependency and constituent parsers, and describe multiple ensemble parsing techniques with their overall effect on the Machine Translation system. We show that parsers' UAS scores are more correlated to the NIST evaluation metric than to the BLEU Metric, however we see increases in both metrics. To truly see the effect of hybrid dependency parsers on machine translation, we will describe and evaluate a combine

Název v anglickém jazyce

Creating Hybrid Dependency Parsers for Syntax-Based MT

Popis výsledku anglicky

Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. In this chapter, we will discuss how different forms and different hybrid combinations of dependency parses effect the overall output of Syntax-Based machine translation both through automatic and manual evaluation. We show results from a variety of individual parsers, including dependency and constituent parsers, and describe multiple ensemble parsing techniques with their overall effect on the Machine Translation system. We show that parsers' UAS scores are more correlated to the NIST evaluation metric than to the BLEU Metric, however we see increases in both metrics. To truly see the effect of hybrid dependency parsers on machine translation, we will describe and evaluate a combine

Druh

C - Kapitola v odborné knize

CEP obor

AI - Jazykověda

OECD FORD obor

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Projekt

<a href="/cs/project/LM2015071" target="_blank" >LM2015071: Jazyková výzkumná infrastruktura v České republice</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Hybrid Approaches to Machine Translation

ISBN

978-3-319-21310-1

Počet stran výsledku

30

Strana od-do

161-190

Počet stran knihy

205

Název nakladatele

Springer International Publishing

Místo vydání

Switzerland

Kód UT WoS kapitoly

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