Expected BLEU Training for Graphs: BBN System Description for WMT11 System Combination Task
Antti-Veikko I. Rosti, Bing Zhang, Spyros Matsoukas, Richard M. Schwartz · 2011
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 multisource combination achieved an 11.1 BLEU point gain. 1