Publication

Detecting Narrativity to Improve English to French Translation of Simple Past Verbs

2013
Conference Papers

Abstract

The correct translation of verb tenses ensures that the temporal ordering of events in the source text is maintained in the target text. This paper assesses the utility of automatically labeling English Simple Past verbs with a binary discursive feature, narrative vs. non-narrative, for statistical machine translation (SMT) into French. The narrativity feature, which helps deciding which of the French past tenses is a correct translation of the English Simple Past, can be assigned with about 70% accuracy (F1). The narrativity feature improves SMT by about 0.2 BLEU points when a factored SMT system is trained and tested on automatically labeled English-French data. More importantly, manual evaluation shows that verb tense translation and verb choice are improved by respectively 9.7% and 3.4% (absolute), leading to an overall improvement of verb translation of 17% (relative).

Official source

https://infoscience.epfl.ch/entities/publication/a08620b2-6806-43c3-b016-bbda6ee0436f

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2013
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a08620b2-6806-43c3-b016-bbda6ee0436f

About this result

Related concepts (32)

Machine translation

Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.

Uses of English verb forms

This article describes the uses of various verb forms in modern standard English language. This includes: Finite verb forms such as go, goes and went Nonfinite forms such as (to) go, going and gone Combinations of such forms with auxiliary verbs, such as was going and would have gone The uses considered include expression of tense (time reference), aspect, mood, modality and voice, in various configurations. For details of how inflected forms of verbs are produced in English, see English verbs.

Translation

Translation is the communication of the meaning of a source-language text by means of an equivalent target-language text. The English language draws a terminological distinction (which does not exist in every language) between translating (a written text) and interpreting (oral or signed communication between users of different languages); under this distinction, translation can begin only after the appearance of writing within a language community.