15,098 research outputs found
Dependency relations as source context in phrase-based SMT
The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical
choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and
supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a DutchâEnglish translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline
Using supertags as source language context in SMT
Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two
enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we
present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two
kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar.
We use a memory-based classification framework that enables the estimation of these features while avoiding
problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an
improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features
A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System
We present the dialogue component of the speech-to-speech translation system
VERBMOBIL. In contrast to conventional dialogue systems it mediates the
dialogue while processing maximally 50% of the dialogue in depth. Special
requirements like robustness and efficiency lead to a 3-layered hybrid
architecture for the dialogue module, using statistics, an automaton and a
planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in
Proceedings of EACL-95, Dublin
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
In interactive machine translation (MT),
human translators correct errors in auto-
matic translations in collaboration with the
MT systems, which is seen as an effective
way to improve the productivity gain in
translation. In this study, we model source-
language syntactic constituency parse and
target-language syntactic descriptions in
the form of supertags as conditional con-
text for interactive prediction in neural
MT (NMT). We found that the supertags
significantly improve productivity gain in
translation in interactive-predictive NMT
(INMT), while syntactic parsing somewhat
found to be effective in reducing human
efforts in translation. Furthermore, when
we model this source- and target-language
syntactic information together as the con-
ditional context, both types complement
each other and our fully syntax-informed
INMT model shows statistically significant
reduction in human efforts for a Frenchâ
toâEnglish translation task in a reference-
simulated setting, achieving 4.30 points
absolute (corresponding to 9.18% relative)
improvement in terms of word prediction
accuracy (WPA) and 4.84 points absolute
(corresponding to 9.01% relative) reduc-
tion in terms of word stroke ratio (WSR)
over the baseline
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