Hybrid System Combination for Machine Translation: An Integration of Phrase-level and Sentences-level Combination Approaches

Given the wide range of successful statistical MT approaches that have emerged recently, it would be beneficial to take advantage of their individual strengths and avoid their individual weaknesses. Multi-Engine Machine Translation (MEMT) attempts to do so by either fusing the output of multiple translation engines or selecting the best translation among them, aiming to improve the overall translation quality. In this thesis, we propose to use the phrase or the sentence as our combination unit instead of the word; three new phrase-level models and one sentence-level model with novel features are proposed. This contrasts with the most popular system combination technique to date which relies on word-level confusion network decoding. Among the three new phrase-level models, the first one utilizes source sentences and target translation hypotheses to learn hierarchical phrases -- phrases that contain subphrases (Chiang 2007). It then re-decodes the source sentences using the hierarchical phrases to combine the results of multiple MT systems. The other two models we propose view combination as a paraphrasing process and use paraphrasing rules. The paraphrasing rules are composed of either string-to-string paraphrases or hierarchical paraphrases, learned from monolingual word alignments between a selected best translation hypothesis and other hypotheses. Our experimental results show that all of the three phrase-level models give superior performance in BLEU compared with the best single translation engine. The two paraphrasing models outperform the re-decoding model and the confusion network baseline model. The sentence-level model exploits more complex syntactic and semantic information than the phrase-level models. It uses consensus, argument alignment, a supertag-based structural language model and a syntactic error detector. We use our sentence-level model in two ways: the first selects a translated sentence from multiple MT systems as the best translation to serve as a backbone for paraphrasing process; the second makes the final decision among all fused translations generated by the phrase-level models and all translated sentences of multiple MT systems. We proposed two novel hybrid combination structures for the integration of phrase-level and sentence-level combination frameworks in order to utilize the advantages of both frameworks and provide a more diverse set of plausible fused translations to consider

Positive Diversity Tuning for Machine Translation System Combination

We present Positive Diversity Tuning, a new method for tuning machine translation models specifically for improved performance during system combination. System combination gains are often limited by the fact that the translations produced by the different component systems are too similar to each other. We propose a method for reducing excess cross-system similarity by optimizing a joint objective that simultaneously rewards models for producing translations that are similar to reference translations, while also punishing them for translations that are too similar to those produced by other systems. The formulation of the Positive Diversity objective is easy to implement and allows for its quick integration with most machine translation tuning pipelines. We find that individual systems tuned on the same data to Positive Diversity can be even more diverse than systems built using different data sets, while still obtaining good BLEU scores. When these individual systems are used together for system combination, our approach allows for significant gains of 0.8 BLEU even when the combination is performed using a small number of otherwise identical individual systems.