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124
Experiments in domain adaptation for statistical machine translation
- Prague, Czech Republic. Association for Computational Linguistics
, 2007
"... The special challenge of the WMT 2007 shared task was domain adaptation. We took this opportunity to experiment with various ways of adapting a statistical machine translation systems to a special domain (here: news commentary), when most of the training data is from a different domain (here: Europe ..."
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Cited by 124 (2 self)
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The special challenge of the WMT 2007 shared task was domain adaptation. We took this opportunity to experiment with various ways of adapting a statistical machine translation systems to a special domain (here: news commentary), when most of the training data is from a different domain (here: European Parliament speeches). This paper also gives a description of the submission of the University of Edinburgh to the shared task. 1 Our framework: the Moses MT system The open source Moses (Koehn et al., 2007) MT system was originally developed at the University
Generalizing word lattice translation
- In ACL-HLT
, 2008
"... redpony, smara, resnik AT umd.edu Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We extend lattice decoding to hierarchical phrase-based models, providing a unified treatment with phrase-ba ..."
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Cited by 71 (11 self)
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redpony, smara, resnik AT umd.edu Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We extend lattice decoding to hierarchical phrase-based models, providing a unified treatment with phrase-based decoding by treating lattices as a case of weighted finite-state automata. In the process, we resolve a significant complication that lattice representations introduce in reordering models. Our experiments evaluating the approach demonstrate substantial gains for Chinese-English and Arabic-English translation.
Discriminative Reordering Models for Statistical Machine Translation
- Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLTNAACL), Workshop on Statistical Machine Translation
, 2006
"... We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the re ..."
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Cited by 54 (13 self)
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We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus. Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art baseline system. 1
Consensus network decoding for statistical machine translation system combination
- In ICASSP
, 2007
"... This paper presents a simple and robust consensus decoding approach for combining multiple Machine Translation (MT) system outputs. A consensus network is constructed from an N-best list by aligning the hypotheses against an alignment reference, where the alignment is based on minimising the transla ..."
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Cited by 48 (9 self)
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This paper presents a simple and robust consensus decoding approach for combining multiple Machine Translation (MT) system outputs. A consensus network is constructed from an N-best list by aligning the hypotheses against an alignment reference, where the alignment is based on minimising the translation edit rate (TER). The Minimum Bayes Risk (MBR) decoding technique is investigated for the selection of an appropriate alignment reference. Several alternative decoding strategies proposed to retain coherent phrases in the original translations. Experimental results are presented primarily based on three-way combination of Chinese-English translation outputs, and also presents results for six-way system combination. It is shown that worthwhile improvements in translation performance can be obtained using the methods discussed. Index Terms — Machine translation, system combination, consensus decoding, Minimum Bayes Risk (MBR) decoding
Word Reordering in Statistical Machine Translation with a POS-Based Distortion Model
"... In this paper we describe a word reordering strategy for statistical machine translation that reorders the source side based on Part of Speech (POS) information. Reordering rules are learned from the word aligned corpus. Reordering is integrated into the decoding process by constructing a lattice, w ..."
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Cited by 34 (3 self)
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In this paper we describe a word reordering strategy for statistical machine translation that reorders the source side based on Part of Speech (POS) information. Reordering rules are learned from the word aligned corpus. Reordering is integrated into the decoding process by constructing a lattice, which contains all word reorderings according to the reordering rules. Probabilities are assigned to the different reorderings. On this lattice monotone decoding is performed. This reordering strategy is compared with our previous reordering strategy, which looks at all permutations within a sliding window. We extend reordering rules by adding context information. Phrase translation pairs are learned from the original corpus and from a reordered source corpus to better capture the reordered word sequences at decoding time. Results are presented for English → Spanish and
Cohesive phrase-based decoding for statistical machine translation
- In Proceedings of ACL-08: HLT
, 2008
"... Phrase-based decoding produces state-of-theart translations with no regard for syntax. We add syntax to this process with a cohesion constraint based on a dependency tree for the source sentence. The constraint allows the decoder to employ arbitrary, non-syntactic phrases, but ensures that those phr ..."
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Cited by 27 (1 self)
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Phrase-based decoding produces state-of-theart translations with no regard for syntax. We add syntax to this process with a cohesion constraint based on a dependency tree for the source sentence. The constraint allows the decoder to employ arbitrary, non-syntactic phrases, but ensures that those phrases are translated in an order that respects the source tree’s structure. In this way, we target the phrasal decoder’s weakness in order modeling, without affecting its strengths. To further increase flexibility, we incorporate cohesion as a decoder feature, creating a soft constraint. The resulting cohesive, phrase-based decoder is shown to produce translations that are preferred over non-cohesive output in both automatic and human evaluations. 1
Learning Linear Ordering Problems for Better Translation
, 2009
"... We apply machine learning to the Linear Ordering Problem in order to learn sentence-specific reordering models for machine translation. We demonstrate that even when these models are used as a mere preprocessing step for German-English translation, they significantly outperform Moses ’ integrated le ..."
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Cited by 27 (0 self)
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We apply machine learning to the Linear Ordering Problem in order to learn sentence-specific reordering models for machine translation. We demonstrate that even when these models are used as a mere preprocessing step for German-English translation, they significantly outperform Moses ’ integrated lexicalized reordering model. Our models are trained on automatically aligned bitext. Their form is simple but novel. They assess, based on features of the input sentence, how strongly each pair of input word tokens wi, wj would like to reverse their relative order. Combining all these pairwise preferences to find the best global reordering is NP-hard. However, we present a non-trivial O(n3) algorithm, based on chart parsing, that at least finds the best reordering within a certain exponentially large neighborhood. We show how to iterate this reordering process within a local search algorithm, which we use in training.
2008b. Pivot Approach for Extracting Paraphrase Patterns from Bilingual Corpora
- In Proceedings of ACL-08:HLT
"... Paraphrase patterns are useful in paraphrase recognition and generation. In this paper, we present a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the English paraphrase patterns are extracted using the sentences in a foreign language as pivots. We propos ..."
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Cited by 24 (3 self)
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Paraphrase patterns are useful in paraphrase recognition and generation. In this paper, we present a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the English paraphrase patterns are extracted using the sentences in a foreign language as pivots. We propose a loglinear model to compute the paraphrase likelihood of two patterns and exploit feature functions based on maximum likelihood estimation (MLE) and lexical weighting (LW). Using the presented method, we extract over 1,000,000 pairs of paraphrase patterns from 2M bilingual sentence pairs, the precision of which exceeds 67%. The evaluation results show that: (1) The pivot approach is effective in extracting paraphrase patterns, which significantly outperforms the conventional method DIRT. Especially, the log-linear model with the proposed feature functions achieves high performance. (2) The coverage of the extracted paraphrase patterns is high, which is above 84%. (3) The extracted paraphrase patterns can be classified into 5 types, which are useful in various applications. 1
An Unsupervised Model for Joint Phrase Alignment and Extraction
"... We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of many granularities are included directly in the model through the use of a novel formulation that memori ..."
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Cited by 23 (5 self)
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We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of many granularities are included directly in the model through the use of a novel formulation that memorizes phrases generated not only by terminal, but also non-terminal symbols. This allows for a completely probabilistic model that is able to create a phrase table that achieves competitive accuracy on phrase-based machine translation tasks directly from unaligned sentence pairs. Experiments on several language pairs demonstrate that the proposed model matches the accuracy of traditional two-step word alignment/phrase extraction approach while reducing the phrase table to a fraction of the original size. 1
Optimizing for sentence-level BLEU+1 yields short translations
- in Proceedings of the 24th International Conference on Computational Linguistics, ser. COLING ’12
, 2012
"... We study a problem with pairwise ranking optimization (PRO): that it tends to yield too short translations. We find that this is partially due to the inadequate smoothing in PRO’s BLEU+1, which boosts the precision component of BLEU but leaves the brevity penalty unchanged, thus destroying the balan ..."
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Cited by 21 (9 self)
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We study a problem with pairwise ranking optimization (PRO): that it tends to yield too short translations. We find that this is partially due to the inadequate smoothing in PRO’s BLEU+1, which boosts the precision component of BLEU but leaves the brevity penalty unchanged, thus destroying the balance between the two, compared to BLEU. It is also partially due to PRO optimizing for a sentence-level score without a global view on the overall length, which introducing a bias towards short translations; we show that letting PRO optimize a corpus-level BLEU yields a perfect length. Finally, we find some residual bias due to the interaction of PRO with BLEU+1: such a bias does not exist for a version of MIRA with sentence-level BLEU+1. We propose several ways to fix the length problem of PRO, including smoothing the brevity penalty, scaling the effective reference length, grounding the precision component, and unclipping the brevity penalty, which yield sizable improvements in test BLEU on two Arabic-English datasets: IWSLT (+0.65) and NIST (+0.37).
