Expected BLEU training for graphs: BBN system description for WMT11 system combination task

Authors:
Antti-Veikko I. Rosti;Bing Zhang;Spyros Matsoukas;Richard Schwartz
Affiliations:
Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA
Venue:
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Year:
2011

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Abstract

BBN submitted system combination outputs for Czech-English, German-English, Spanish-English, and French-English language pairs. All combinations were based on confusion network decoding. The confusion networks were built using incremental hypothesis alignment algorithm with flexible matching. A novel bi-gram count feature, which can penalize bi-grams not present in the input hypotheses corresponding to a source sentence, was introduced in addition to the usual decoder features. The system combination weights were tuned using a graph based expected BLEU as the objective function while incrementally expanding the networks to bi-gram and 5-gram contexts. The expected BLEU tuning described in this paper naturally generalizes to hypergraphs and can be used to optimize thousands of weights. The combination gained about 0.5-4.0 BLEU points over the best individual systems on the official WMT11 language pairs. A 39 system multi-source combination achieved an 11.1 BLEU point gain.