Itzulpen automatikoa

Multiword Expressions (MWEs) are idiosyncratic combinations of words which pose important challenges to Natural Language Processing. Some kinds of MWEs, such as verbal ones, are particularly hard to identify in corpora, due to their high degree of morphosyntactic flexibility. This paper describes a linguistically motivated method to gather detailed information about verb+noun MWEs (VNMWEs) from corpora. Although the main focus of this study is Spanish, the method is easily adaptable to other languages.

Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtranslated data from different sources has led to better results than when using such data in isolation. In this work we analyse the impact that data translated with rule-based, phrase-based statistical and neural MT systems has on new MT systems.

This article describes a compositional distributional method to generate contextualized senses of words and identify their appropriate translations in the target language using monolingual corpora. Word translation is modeled in the same way as contextualization of word meaning, but in a bilingual vector space. The contextualization of meaning is carried out by means of distributional composition within a structured vector space with syntactic dependencies, and the bilingual space is created by means of transfer rules and a bilingual dictionary.

This is a summary of the PhD thesis written by Uxoa Iñurrieta under the supervision of Dr. Gorka Labaka and Dr. Itziar Aduriz. Full title of the PhD thesis in Basque: "Izena+aditza Unitate Fraseologikoak gaztelaniatik euskarara: azterketa eta tratamendu konputazionala". The defense was held in San Sebastian on November 29, 2019. The doctoral committee was integrated by Ricardo Etxepare (Centre National de la Recherche Scientifique), Margarita Alonso (Universidad de Coruña) and Miren Azkarate (University of the Basque Country).