316 research outputs found
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
Modeling Target-Side Inflection in Neural Machine Translation
NMT systems have problems with large vocabulary sizes. Byte-pair encoding
(BPE) is a popular approach to solving this problem, but while BPE allows the
system to generate any target-side word, it does not enable effective
generalization over the rich vocabulary in morphologically rich languages with
strong inflectional phenomena. We introduce a simple approach to overcome this
problem by training a system to produce the lemma of a word and its
morphologically rich POS tag, which is then followed by a deterministic
generation step. We apply this strategy for English-Czech and English-German
translation scenarios, obtaining improvements in both settings. We furthermore
show that the improvement is not due to only adding explicit morphological
information.Comment: Accepted as a research paper at WMT17. (Updated version with
corrected references.
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
Syntax-to-morphology alignment and constituent reordering in factored phrase-based statistical machine translation from English to Turkish
English is a moderately analytic language in which the meaning is conveyed with function words and the order of constituents. On the other hand, Turkish is an agglutinative language with free constituent order. These differences together with the lack of large scale English-Turkish parallel corpora turn Statistical Machine Translation (SMT) between these languages into a challenging problem. SMT between these two languages, especially from English to Turkish has been worked on for several years. The initial findings [El-Kahlout and Of lazer, 2006] strongly support the idea of representing both Turkish and English at the morpheme-level. Furthermore, several representations and groupings for the morphological structure have been tried on the Turkish side. In contrast to these, this thesis mostly focuses on the experiments on the English side rather than Turkish. In this work we firstly introduce a new way to align the English syntax with the Turkish morphology by associating function words to their related content words. This transformation solely depends on the dependency relations between these words. In addition to this improved alignment, a syntactic reordering is performed to get a more monotonic word alignment. Here, we again use dependencies to identify the sentence constituents and perform reordering between them so that the word order of the source side will be close to the target language. We report our results with BLEU which is a measure that is widely used by the MT community to report research results. With improvements in the alignment and the ordering, we have increased our BLEU score from a baseline score of 17.08 to 23.78, which is an improvement of 6.7 BLEU points, or about 39% relative
Improving subject-verb agreement in SMT
Ensuring agreement between the subject and the main verb is crucial for the correctness of the information that a sentence conveys. While generating correct subject-verb agreement
is relatively straightforward in rule-based approaches to Machine Translation (RBMT), today’s
leading statistical Machine Translation (SMT) systems often fail to generate correct subject-verb
agreements, especially when the target language is morphologically richer than the source language. The main problem is that one surface verb form in the source language corresponds to
many surface verb forms in the target language. To deal with subject-verb agreement we built a
hybrid SMT system that augments source verbs with extra linguistic information drawn from their
source-language context. This information, in the form of labels attached to verbs that indicate
person and number, creates a closer association between a verb from the source and a verb in the
target language. We used our preprocessing approach on English as source language and built an
SMT system for translation to French. In a range of experiments, the results show improvements
in translation quality for our augmented SMT system over a Moses baseline engine, on both automatic and manual evaluations, for the majority of cases where the subject-verb agreement was
previously incorrectly translated
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