Publication

A Corpus-based Contrastive Analysis for Defining Minimal Semantics of Inter-sentential Dependencies for Machine Translation

2011
Conference Papers

Abstract

Inter-sentential dependencies such as discourse connectives or pronouns have an impact on the translation of these items. These dependencies have classically been analyzed within complex theoretical frameworks, often monolingual ones, and the resulting fine-grained descriptions, although relevant to translation, are likely beyond reach of statistical machine translation systems. Instead, we propose an approach to search for a minimal, feature-based characterization of translation divergencies due to inter-sentential dependencies, in the case of discourse connectives and pronouns, based on contrastive analyses performed on the Europarl corpus. In addition, we show how to automatically assign labels to connectives and pronouns, and how to use them for statistical machine translation.

Official source

https://infoscience.epfl.ch/entities/publication/383addfd-e863-4bda-a492-3c2904a354d9

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.

2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/383addfd-e863-4bda-a492-3c2904a354d9

About this result

Related concepts (32)

Statistical machine translation

Statistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.

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.

Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance.

Related publications (16)

A Corpus-based Contrastive Analysis for Defining Minimal Semantics of Inter-sentential Dependencies for Machine Translation

Chat with Graph Search

Moving Beyond Review: Applying Language Models to Planning and Translation in Reflection

One degree of freedom linear stages with minimised parasitic shift

SEFI Handbook on Teaching Transferable Competencies and Skills in Engineering