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98
A survey of statistical machine translation
, 2007
"... Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular tec ..."
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Cited by 93 (6 self)
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Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular techniques have only emerged within the last few years. This survey presents a tutorial overview of state-of-the-art SMT at the beginning of 2007. We begin with the context of the current research, and then move to a formal problem description and an overview of the four main subproblems: translational equivalence modeling, mathematical modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and notes on future directions.
A tree sequence alignment-based tree-to-tree translation model
- In Proceedings of ACL
, 2008
"... This paper presents a translation model that is based on tree sequence alignment, where a tree sequence refers to a single sequence of subtrees that covers a phrase. The model leverages on the strengths of both phrase-based and linguistically syntax-based method. It automatically learns aligned tree ..."
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Cited by 42 (2 self)
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This paper presents a translation model that is based on tree sequence alignment, where a tree sequence refers to a single sequence of subtrees that covers a phrase. The model leverages on the strengths of both phrase-based and linguistically syntax-based method. It automatically learns aligned tree sequence pairs with mapping probabilities from word-aligned biparsed parallel texts. Compared with previous models, it not only captures non-syntactic phrases and discontinuous phrases with linguistically structured features, but also supports multi-level structure reordering of tree typology with larger span. This gives our model stronger expressive power than other reported models. Experimental results on the NIST MT-2005 Chinese-English translation task show that our method statistically significantly outperforms the baseline systems. 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.
Improving Statistical Machine Translation using Lexicalized Rule Selection
- In Proc. Coling
, 2008
"... This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule ..."
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Cited by 24 (9 self)
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This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule selection models into the state-of-the-art syntax-based SMT model. Experiments show that our lexicalized approach for rule selection achieves statistically significant improvements over the state-of-the-art SMT system. 1
A Discriminative Syntactic Word Order Model for Machine Translation
"... We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that combining discriminative training with features to detec ..."
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Cited by 15 (0 self)
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We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that combining discriminative training with features to detect these two different kinds of movement phenomena leads to substantial improvements in word ordering performance over strong baselines. Integrating this word order model in a baseline MT system results in a 2.4 points improvement in BLEU for English to Japanese translation. 1
Learning Translation Boundaries for Phrase-Based Decoding
"... Constrained decoding is of great importance not only for speed but also for translation quality. Previous efforts explore soft syntactic constraints which are based on constituent boundaries deduced from parse trees of the source language. We present a new framework to establish soft constraints bas ..."
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Cited by 14 (3 self)
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Constrained decoding is of great importance not only for speed but also for translation quality. Previous efforts explore soft syntactic constraints which are based on constituent boundaries deduced from parse trees of the source language. We present a new framework to establish soft constraints based on a more natural alternative: translation boundary rather than constituent boundary. We propose simple classifiers to learn translation boundaries for any source sentences. The classifiers are trained directly on word-aligned corpus without using any additional resources. We report the accuracy of our translation boundary classifiers. We show that using constraints based on translation boundaries predicted by our classifiers achieves significant improvements over the baseline on large-scale Chinese-to-English translation experiments. The new constraints also significantly outperform constituent boundary based syntactic constrains. 1
Enhancing language models in statistical machine translation with backward n-grams and mutual information triggers
- In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
, 2011
"... Abstract In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language m ..."
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Cited by 13 (4 self)
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Abstract In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We integrate the two proposed models into phrase-based statistical machine translation and conduct experiments on large-scale training data to investigate their effectiveness. Our experimental results show that both models are able to significantly improve translation quality and collectively achieve up to 1 BLEU point over a competitive baseline.
Ordering Phrases with Function Words
"... This paper presents a Function Word centered, Syntax-based (FWS) solution to address phrase ordering in the context of statistical machine translation (SMT). Motivated by the observation that function words often encode grammatical relationship among phrases within a sentence, we propose a probabili ..."
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Cited by 12 (7 self)
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This paper presents a Function Word centered, Syntax-based (FWS) solution to address phrase ordering in the context of statistical machine translation (SMT). Motivated by the observation that function words often encode grammatical relationship among phrases within a sentence, we propose a probabilistic synchronous grammar to model the ordering of function words and their left and right arguments. We improve phrase ordering performance by lexicalizing the resulting rules in a small number of cases corresponding to function words. The experiments show that the FWS approach consistently outperforms the baseline system in ordering function words ’ arguments and improving translation quality in both perfect and noisy word alignment scenarios. 1
A Recursive Recurrent Neural Network for Statistical Machine Translation
"... In this paper, we propose a novel recursive recurrent neural network (R2NN) to mod-el the end-to-end decoding process for s-tatistical machine translation. R2NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new in ..."
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Cited by 11 (1 self)
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In this paper, we propose a novel recursive recurrent neural network (R2NN) to mod-el the end-to-end decoding process for s-tatistical machine translation. R2NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, like recurrent neu-ral networks, so that language model and translation model can be integrated natu-rally; (2) a tree structure can be built, as recursive neural networks, so as to gener-ate the translation candidates in a bottom up manner. A semi-supervised training ap-proach is proposed to train the parameter-s, and the phrase pair embedding is ex-plored to model translation confidence di-rectly. Experiments on a Chinese to En-glish translation task show that our pro-posed R2NN can outperform the state-of-the-art baseline by about 1.5 points in BLEU.
Extracting preordering rules from predicate-argument structures
, 2011
"... Word ordering remains as an essential problem for translating between languages with substantial structural differences, such as SOV and SVO languages. In this paper, we propose to automatically extract pre-ordering rules from predicateargument structures. A pre-ordering rule records the relative po ..."
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Cited by 9 (2 self)
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Word ordering remains as an essential problem for translating between languages with substantial structural differences, such as SOV and SVO languages. In this paper, we propose to automatically extract pre-ordering rules from predicateargument structures. A pre-ordering rule records the relative position mapping of a predicate word and its argument phrases from the source language side to the target language side. We propose 1) a lineartime algorithm to extract the pre-ordering rules from word-aligned HPSG-tree-tostring pairs and 2) a bottom-up algorithm to apply the extracted rules to HPSG trees to yield target language style source sentences. Experimental results are reported for large-scale English-to-Japanese translation, showing significant improvements of BLEU score compared with the baseline SMT systems. 1
