CUNI-Malta system at CoNLL-SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405559" target="_blank" >RIV/00216208:11320/19:10405559 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.aclweb.org/anthology/W19-4213/" target="_blank" >https://www.aclweb.org/anthology/W19-4213/</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

CUNI-Malta system at CoNLL-SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation

Popis výsledku v původním jazyce

This paper presents the submission by the Charles University-University of Malta team to the CoNLL--SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context. We present a lemmatization model based on previous work on neural transducers cite{makarov2018neural,aharoni-goldberg-2017-morphological}. The key difference is that our model transform the whole word form in every stem, instead of consuming it character by character. We propose a merging strategy inspired by Byte-Pair-Encoding that reduces the space of valid operations by merging frequent adjacent operations. The resulting operations not only encode the action/s to be performed but the relative position in the word token and how characters need to be transformed. Our morphological tagger is a vanilla biLSTM tagger that operates over operation representations, encoding operations and words in a hierarchical manner. Even though relative performance according to metrics is below the baseline, experiments show that our mod

Název v anglickém jazyce

CUNI-Malta system at CoNLL-SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation

Popis výsledku anglicky

This paper presents the submission by the Charles University-University of Malta team to the CoNLL--SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context. We present a lemmatization model based on previous work on neural transducers cite{makarov2018neural,aharoni-goldberg-2017-morphological}. The key difference is that our model transform the whole word form in every stem, instead of consuming it character by character. We propose a merging strategy inspired by Byte-Pair-Encoding that reduces the space of valid operations by merging frequent adjacent operations. The resulting operations not only encode the action/s to be performed but the relative position in the word token and how characters need to be transformed. Our morphological tagger is a vanilla biLSTM tagger that operates over operation representations, encoding operations and words in a hierarchical manner. Even though relative performance according to metrics is below the baseline, experiments show that our mod

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 16th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

ISBN

978-1-950737-36-9

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

104-112

Název nakladatele

Association for Computational Linguistics

Místo vydání

Stroudsburg, PA, USA

Místo konání akce

Firenze, Italy

Datum konání akce

2. 8. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—