1,226 research outputs found

    Integrated Parallel Sentence and Fragment Extraction from Comparable Corpora: A Case Study on Chinese--Japanese Wikipedia

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    Parallel corpora are crucial for statistical machine translation (SMT); however, they are quite scarce for most language pairs and domains. As comparable corpora are far more available, many studies have been conducted to extract either parallel sentences or fragments from them for SMT. In this article, we propose an integrated system to extract both parallel sentences and fragments from comparable corpora. We first apply parallel sentence extraction to identify parallel sentences from comparable sentences. We then extract parallel fragments from the comparable sentences. Parallel sentence extraction is based on a parallel sentence candidate filter and classifier for parallel sentence identification. We improve it by proposing a novel filtering strategy and three novel feature sets for classification. Previous studies have found it difficult to accurately extract parallel fragments from comparable sentences. We propose an accurate parallel fragment extraction method that uses an alignment model to locate the parallel fragment candidates and an accurate lexicon-based filter to identify the truly parallel fragments. A case study on the Chinese--Japanese Wikipedia indicates that our proposed methods outperform previously proposed methods, and the parallel data extracted by our system significantly improves SMT performance

    The Circle of Meaning: From Translation to Paraphrasing and Back

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    The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection

    Multilingual Spoken Language Translation

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