5,323 research outputs found
How much hybridisation does machine translation need?
This is the peer reviewed version of the following article: [Costa-jussà, M. R. (2015), How much hybridization does machine translation Need?. J Assn Inf Sci Tec, 66: 2160–2165. doi:10.1002/asi.23517], which has been published in final form at [10.1002/asi.23517]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Rule-based and corpus-based machine translation (MT)have coexisted for more than 20 years. Recently, bound-aries between the two paradigms have narrowed andhybrid approaches are gaining interest from bothacademia and businesses. However, since hybridapproaches involve the multidisciplinary interaction oflinguists, computer scientists, engineers, and informa-tion specialists, understandably a number of issuesexist.While statistical methods currently dominate researchwork in MT, most commercial MT systems are techni-cally hybrid systems. The research community shouldinvestigate the bene¿ts and questions surrounding thehybridization of MT systems more actively. This paperdiscusses various issues related to hybrid MT includingits origins, architectures, achievements, and frustra-tions experienced in the community. It can be said thatboth rule-based and corpus- based MT systems havebene¿ted from hybridization when effectively integrated.In fact, many of the current rule/corpus-based MTapproaches are already hybridized since they do includestatistics/rules at some point.Peer ReviewedPostprint (author's final draft
Compositional Morphology for Word Representations and Language Modelling
This paper presents a scalable method for integrating compositional
morphological representations into a vector-based probabilistic language model.
Our approach is evaluated in the context of log-bilinear language models,
rendered suitably efficient for implementation inside a machine translation
decoder by factoring the vocabulary. We perform both intrinsic and extrinsic
evaluations, presenting results on a range of languages which demonstrate that
our model learns morphological representations that both perform well on word
similarity tasks and lead to substantial reductions in perplexity. When used
for translation into morphologically rich languages with large vocabularies,
our models obtain improvements of up to 1.2 BLEU points relative to a baseline
system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning
(ICML
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
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
A syntactified direct translation model with linear-time decoding
Recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. The decoders accompanying these extensions typically exceed quadratic time complexity. This paper extends the Direct Translation Model 2 (DTM2) with syntax while maintaining linear-time decoding. We employ a linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar
(CCG). As every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single
supertag–operator pair for extending the dependency parse incrementally. Alongside translation features extracted from
the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly
outperforms the state-of-the art DTM2 system
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