Discourse in Statistical Machine Translation
نویسندگان
چکیده
Avertissement Le contenu de ce site relève de la législation française sur la propriété intellectuelle et est la propriété exclusive de l'éditeur. Les œuvres figurant sur ce site peuvent être consultées et reproduites sur un support papier ou numérique sous réserve qu'elles soient strictement réservées à un usage soit personnel, soit scientifique ou pédagogique excluant toute exploitation commerciale. La reproduction devra obligatoirement mentionner l'éditeur, le nom de la revue, l'auteur et la référence du document. Toute autre reproduction est interdite sauf accord préalable de l'éditeur, en dehors des cas prévus par la législation en vigueur en France.
منابع مشابه
Towards a discourse relation-aware approach for Chinese-English machine translation
Translation of discourse relations is one of the recent efforts of incorporating discourse information to statistical machine translation (SMT). While existing works focus on disambiguation of ambiguous discourse connectives, or transformation of discourse trees, only explicit discourse relations are tackled. A greater challenge exists in machine translation of Chinese, since implicit discourse...
متن کاملDiscourse-level features for statistical machine translation
The talk will show how the disambiguation of discourse connectives can improve their automatic translation. Connectives are a class of frequent functional lexical items that play an important role in text readability and coherence. Longer-range context is taken into account to learn the signaled rhetorical relations. The labels obtained from a discourse connective classifier are then integrated...
متن کاملDisambiguating Temporal–Contrastive Discourse Connectives for Machine Translation
Temporal–contrastive discourse connectives (although, while, since, etc.) signal various types of relations between clauses such as temporal, contrast, concession and cause. They are often ambiguous and therefore difficult to translate from one language to another. We discuss several new and translation-oriented experiments for the disambiguation of a specific subset of discourse connectives in...
متن کاملAutomatic Mapping of French Discourse Connectives to PDTB Discourse Relations
In this paper, we present an approach to exploit phrase tables generated by statistical machine translation in order to map French discourse connectives to discourse relations. Using this approach, we created ConcoLeDisCo, a lexicon of French discourse connectives and their PDTB relations. When evaluated against LEXCONN, ConcoLeDisCo achieves a recall of 0.81 and an Average Precision of 0.68 fo...
متن کاملUsing Sense-labeled Discourse Connectives for Statistical Machine Translation
This article shows how the automatic disambiguation of discourse connectives can improve Statistical Machine Translation (SMT) from English to French. Connectives are firstly disambiguated in terms of the discourse relation they signal between segments. Several classifiers trained using syntactic and semantic features reach stateof-the-art performance, with F1 scores of 0.6 to 0.8 over thirteen...
متن کاملImplicitation of Discourse Connectives in (Machine) Translation
Explicit discourse connectives in a source language text are not always translated to comparable words or phrases in the target language. The paper provides a corpus analysis and a method for semi-automatic detection of such cases. Results show that discourse connectives are not translated into comparable forms (or even any form at all), in up to 18% of human reference translations from English...
متن کامل