Phrase-based Translation Research Articles

Chinese-Uyghur machine translation system for phrase-based statistical translation

This paper described a Chinese-Uyghur machine translation system for phrase-based statistical translation.First the Chinese-Uyghur corpus was used to train the language model and the translation model.After using these models to decode the source statement,the authors got the best translation statement.The core decoding algorithm was the beam search algorithm,and the Latin-Script Uyghur was used as the Uyghur corpus.The experimental results show that phrase-based statistical machine translation method can be used quickly and effectively to build a Chinese-Uygher machine translation platform.

Discriminative Machine Translation Using Global Lexical Selection

Statistical phrase-based machine translation models crucially rely on word alignments. The search for word-alignments assumes a model of word locality between source and target languages that is violated in starkly different word-order languages such as English-Hindi. In this article, we present models that decouple the steps of lexical selection and lexical reordering with the aim of minimizing the role of word-alignment in machine translation. Indian languages are morphologically rich and have relatively free-word order where the grammatical role of content words is largely determined by their case markers and not just by their positions in the sentence. Hence, lexical selection plays a far greater role than lexical reordering. For lexical selection, we investigate models that take the entire source sentence into account and evaluate their performance for English-Hindi translation in a tourism domain.

Efficient Speech Translation Through Confusion Network Decoding

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents a decoding algorithm for confusion networks which results as an extension of a state-of-the-art phrase-based text translation decoder. The confusion network decoder significantly improves both in efficiency and performance over previous work along this direction, and outperforms the background text translation system. Experimental results in terms of translation accuracy and decoding efficiency are reported for the task of translating plenary speeches of the European Parliament from Spanish to english and from english to Spanish.

System Combination for Machine Translation of Spoken and Written Language

This paper describes an approach for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The consensus translation is computed by weighted majority voting on a confusion network, similarly to the well-established ROVER approach of Fiscus for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original MT hypotheses are learned using an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole corpus of automatic translations rather than a single sentence is taken into account in order to achieve high alignment quality. The confusion network is rescored with a special language model, and the consensus translation is extracted as the best path. The proposed system combination approach was evaluated in the framework of the TC-STAR speech translation project. Up to six state-of-the-art statistical phrase-based translation systems from different project partners were combined in the experiments. Significant improvements in translation quality from Spanish to English and from English to Spanish in comparison with the best of the individual MT systems were achieved under official evaluation conditions.

The scaling problem in the pattern recognition approach to machine translation

Statistical machine translation (SMT) has proven to be an interesting pattern recognition framework for automatically building machine translations systems from available parallel corpora. In the last few years, research in SMT has been characterized by two significant advances. First, the popularization of the so called phrase-based statistical translation models, which allows to incorporate local contextual information to the translation models. Second, the availability of larger and larger parallel corpora, which are composed of millions of sentence pairs, and tens of millions of running words. Since phrase-based models basically consists in statistical dictionaries of phrase pairs, their estimation from very large corpora is a very costly task that yields a huge number of parameters which are to be stored in memory. The handling of millions of model parameters and a similar number of training samples have become a bottleneck in the field of SMT, as well as in other well-known pattern recognition tasks such as speech recognition or handwritten recognition, just to name a few. In this paper, we propose a general framework that deals with the scaling problem in SMT without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.

A block bigram prediction model for statistical machine translation

In this article, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT). The model predicts block neighbors to carry out a phrase-based translation that explicitly handles local phrase reordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g., a language model score) as well as binary features based on the block identities themselves (e.g., block bigram features). The model training relies on an efficient enumeration of local block neighbors in parallel training data. A novel stochastic gradient descent (SGD) training algorithm is presented that can easily handle millions of features. Moreover, when viewing SMT as a block generation process, it becomes quite similar to sequential natural language annotation problems such as part-of-speech tagging, phrase chunking, or shallow parsing. Our novel approach is successfully tested on a standard Arabic-English translation task using two different phrase reordering models: a block orientation model and a phrase-distortion model.

Hierarchical Phrase-Based Translation

We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from both syntax-based translation and phrase-based translation. We describe our system's training and decoding methods in detail, and evaluate it for translation speed and translation accuracy. Using BLEU as a metric of translation accuracy, we find that our system performs significantly better than the Alignment Template System, a state-of-the-art phrase-based system.

EBMT by tree-phrasing

This article presents an attempt to build a repository storing associations between simple syntactic dependency treelets in a source language and their corresponding phrases in a target language. We assess the usefulness of this resource in two different settings. First, we show that it improves upon a standard subsentential translation memory. Second, we observe improvements in translation quality when a standard statistical phrase-based translation engine is augmented with the ability to exploit such a repository.

A word-to-phrase statistical translation model

This article addresses the development of statistical models for phrase-based machine translation (MT) which extend a popular word-alignment model proposed by IBM in the early 90s. A novel decoding algorithm is directly derived from the optimization criterion which defines the statistical MT approach. Efficiency in decoding is achieved by applying dynamic programming, pruning strategies, and word reordering constraints. It is known that translation performance can be boosted by exploiting phrase (or multiword) translation pairs automatically extracted from a parallel corpus. New phrase-based models are obtained by introducing extra multiwords in the target language vocabulary and by estimating the corresponding parameters from either: (i) a word-based model, (ii) phrase-based statistics computed on the parallel corpus, or (iii) the interpolation of the two previous estimates. Word-based and phrase-based MT models are evaluated on a traveling domain task in two translation directions: Chinese-English (12k-word vocabulary) and Italian-English (16k-word vocabulary). Phrase-based models show Bleu score improvements over the word-based model by 19% and 13% relative, respectively.

Mandarin–English Information (MEI): investigating translingual speech retrieval

This paper describes the Mandarin–English Information (MEI) project, where we investigated the problem of cross-language spoken document retrieval (CL-SDR), and developed one of the first English–Chinese CL-SDR systems. Our system accepts an entire English news story (text) as query, and retrieves relevant Chinese broadcast news stories (audio) from the document collection. Hence, this is a cross-language and cross-media retrieval task. We applied a multi-scale approach to our problem, which unifies the use of phrases, words and subwords in retrieval. The English queries are translated into Chinese by means of a dictionary-based approach, where we have integrated phrase-based translation with word-by-word translation. Untranslatable named entities are transliterated by a novel subword translation technique. The multi-scale approach can be divided into three subtasks – multi-scale query formulation, multi-scale audio indexing (by speech recognition) and multi-scale retrieval. Experimental results demonstrate that the use of phrase-based translation and subword translation gave performance gains, and multi-scale retrieval outperforms word-based retrieval.