4,125 research outputs found
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
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Identifying Semantic Divergences in Parallel Text without Annotations
Recognizing that even correct translations are not always semantically
equivalent, we automatically detect meaning divergences in parallel sentence
pairs with a deep neural model of bilingual semantic similarity which can be
trained for any parallel corpus without any manual annotation. We show that our
semantic model detects divergences more accurately than models based on surface
features derived from word alignments, and that these divergences matter for
neural machine translation.Comment: Accepted as a full paper to NAACL 201
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate between many languages at almost no additional cost other than
training time. Furthermore, internal representations learned by the network
serve as a new semantic representation of words -or sentences- which, unlike
standard word embeddings, are learned in an essentially bilingual or even
multilingual context. In view of these properties, the contribution of the
present work is two-fold. First, we systematically study the NMT context
vectors, i.e. output of the encoder, and their power as an interlingua
representation of a sentence. We assess their quality and effectiveness by
measuring similarities across translations, as well as semantically related and
semantically unrelated sentence pairs. Second, as extrinsic evaluation of the
first point, we identify parallel sentences in comparable corpora, obtaining an
F1=98.2% on data from a shared task when using only NMT context vectors. Using
context vectors jointly with similarity measures F1 reaches 98.9%.Comment: 11 pages, 4 figure
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