59,135 research outputs found
Supertagged phrase-based statistical machine translation
Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system performance to deteriorate. In this work we show that incorporating lexical syntactic descriptions in the form of supertags can yield significantly better PBSMT systems. We describe a novel PBSMT model that integrates
supertags into the target language model and the target side of the translation model. Two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar
and Combinatory Categorial Grammar. Despite the differences between these two approaches, the supertaggers give similar improvements. In addition to supertagging, we also explore the utility of a surface global grammaticality measure based on combinatory operators. We perform various experiments on the Arabic to English NIST 2005 test set addressing issues such as sparseness, scalability and the utility of system subcomponents. Our best result (0.4688 BLEU) improves by 6.1% relative to a state-of-theart
PBSMT model, which compares very favourably with the leading systems on the NIST 2005 task
Syntactic phrase-based statistical machine translation
Phrase-based statistical machine translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 3.51% relative) on the NIST 2003 Arabic-English test corpus
Accuracy-based scoring for phrase-based statistical machine translation
Although the scoring features of state-of-the-art Phrase-Based Statistical Machine Translation (PB-SMT) models are weighted so as to optimise an objective function measuring
translation quality, the estimation of the features
themselves does not have any relation to such quality metrics. In this paper, we introduce a translation quality-based feature to PBSMT in a bid to improve the translation quality of the system. Our feature is estimated by averaging
the edit-distance between phrase pairs involved in the translation of oracle sentences, chosen by automatic evaluation metrics from the N-best outputs of a baseline system, and phrase pairs occurring in the N-best list. Using
our method, we report a statistically significant 2.11% relative improvement in BLEU score for the WMT 2009 Spanish-to-English translation task. We also report that using our
method we can achieve statistically significant improvements over the baseline using many other MT evaluation metrics, and a substantial increase in speed and reduction in memory use (due to a reduction in phrase-table size of 87%) while maintaining significant gains in
translation quality
Parallel Treebanks in Phrase-Based Statistical Machine Translation
Given much recent discussion and the shift in focus of the field, it is becoming apparent that the incorporation of syntax is the way forward for the current state-of-the-art in machine translation (MT). Parallel treebanks are a relatively recent innovation and appear to be ideal candidates for MT training material. However, until recently there has been no other means to build them than by
hand. In this paper, we describe how we make use of new tools to automatically build a large parallel treebank and extract a set of linguistically motivated phrase pairs from it. We show that adding these phrase pairs to the translation model of a baseline phrase-based statistical MT (PBSMT) system leads to significant improvements in translation quality. We describe further experiments on incorporating parallel treebank information into PBSMT, such as word alignments. We investigate the conditions under which the incorporation of parallel treebank data performs optimally. Finally, we discuss the potential of parallel treebanks in other paradigms of MT
Learning Semantic Representations for the Phrase Translation Model
This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is performed by a multi-layer neural network whose
weights are learned on parallel training data. The learning is aimed to
directly optimize the quality of end-to-end machine translation results.
Experimental evaluation has been performed on two Europarl translation tasks,
English-French and German-English. The results show that the new semantic-based
phrase translation model significantly improves the performance of a
state-of-the-art phrase-based statistical machine translation sys-tem, leading
to a gain of 0.7-1.0 BLEU points
Extended Parallel Corpus for Amharic-English Machine Translation
This paper describes the acquisition, preprocessing, segmentation, and
alignment of an Amharic-English parallel corpus. It will be useful for machine
translation of an under-resourced language, Amharic. The corpus is larger than
previously compiled corpora; it is released for research purposes. We trained
neural machine translation and phrase-based statistical machine translation
models using the corpus. In the automatic evaluation, neural machine
translation models outperform phrase-based statistical machine translation
models.Comment: Accepted to 2nd AfricanNLP workshop at EACL 202
A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences
This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and
English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the
systems generate different output and can potentially
be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging
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