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.

Abstract. We present a novel, simple, unsupervised method for characterizing the semantic relations that hold between nouns in noun-noun compounds. The main idea is to discover predicates that make explicit the hidden relations between the nouns. This is accomplished by writing Web search engine queries that restate the noun compound as a relative clause containing a wildcard character to be filled in with a verb. A comparison to results from the literature suggest this is a promising approach.

In this paper, we present ParaEval, an automatic evaluation framework that uses paraphrases to improve the quality of machine translation evaluations. Previous work has focused on fixed n-gram evaluation metrics coupled with lexical identity matching. ParaEval addresses three important issues: support for paraphrase/synonym matching, recall measurement, and correlation with human judgments. We show that ParaEval correlates significantly better than BLEU with human assessment in measurements for both fluency and adequacy. 1

Context-Based Machine Translation™ (CBMT) is a new paradigm for corpusbased translation that requires no parallel text. Instead, CBMT relies on a lightweight translation model utilizing a fullform bilingual dictionary and a sophisticated decoder using long-range context via long n-grams and cascaded overlapping. The translation process is enhanced via in-language substitution of tokens and phrases, both for source and target, when top candidates cannot be confirmed or resolved in decoding. Substitution utilizes a synonym and near-synonym generator implemented as a corpus-based unsupervised learning process. Decoding requires a very large target-language-only corpus, and while substitution in target can be performed using that same corpus, substitution in source requires a separate (and smaller) source monolingual corpus. Spanish-to-English CBMT was tested on Spanish newswire text, achieving a BLEU score of 0.6462 in June 2006, the highest BLEU reported for any language pair. Further testing also shows that quality increases above the reported score as the target corpus size increases and as dictionary coverage of source words and phrases becomes more complete. 1 1

Paraphrases have proved to be useful in many applications, including Machine Translation, Question Answering, Summarization, and Information Retrieval. Paraphrase acquisition methods that use a single monolingual corpus often produce only syntactic paraphrases. We present a method for obtaining surface paraphrases, using a 150GB (25 billion words) monolingual corpus. Our method achieves an accuracy of around 70 % on the paraphrase acquisition task. We further show that we can use these paraphrases to generate surface patterns for relation extraction. Our patterns are much more precise than those obtained by using a state of the art baseline and can extract relations with more than 80 % precision for each of the test relations. 1

Current phrase-based SMT systems perform poorly when using small training sets. This is a consequence of unreliable translation estimates and low coverage over source and target phrases. This paper presents a method which alleviates this problem by exploiting multiple translations of the same source phrase. Central to our approach is triangulation, the process of translating from a source to a target language via an intermediate third language. This allows the use of a much wider range of parallel corpora for training, and can be combined with a standard phrase-table using conventional smoothing methods. Experimental results demonstrate BLEU improvements for triangulated models over a standard phrase-based system. 1

The source text provided to a machine translation system is typically only one of many ways the input sentence could have been expressed, and alternative forms of expression can often produce a better translation. We introduce here error driven paraphrasing of source sentences: instead of paraphrasing a source sentence exhaustively, we obtain paraphrases for only the parts that are predicted to be problematic for the translation system. We report on an Amazon Mechanical Turk study that explores this idea, and establishes via an oracle evaluation that it holds the potential to substantially improve translation quality. 1

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

Automatic paraphrasing is an important component in many natural language processing tasks. In this paper we present a new parallel corpus with paraphrase annotations. We adopt a definition of paraphrase based on word-alignments and show that it yields high inter-annotator agreement. As Kappa is suited to nominal data, we employ an alternative agreement statistic which is appropriate for structured alignment tasks. We discuss how the corpus can be usefully employed in evaluating paraphrase systems automatically (e.g., by measuring precision, recall and F1) and also in developing linguistically rich paraphrase models based on syntactic structure. 1.

We present a comparison of two approaches for Arabic-Chinese machine translation using English as a pivot language: sentence pivoting and phrase-table pivoting. Our results show that using English as a pivot in either approach outperforms direct translation from Arabic to Chinese. Our best result is the phrase-pivot system which scores higher than direct translation by 1.1 BLEU points. An error analysis of our best system shows that we successfully handle many complex Arabic-Chinese syntactic variations. 1