2 . 8 . 2 . Confusion Network Decoding for MT System Combination

Confusion network decoding has been very successful in combining speech-to-text (STT) outputs (Fiscus 1997; Evermann and Woodland, 2000; Mangu et al., 2000) from diverse systems using different modeling assumptions. Several modeling paradigms have been introduced in machine translation (MT) including rule-based, phrase-based, hierarchical, syntax-based and even cascades of rule-based and statistical MT systems. Building confusion networks from MT system outputs is more challenging compared to STT system outputs since the translations may have very different word orders and varying lexical choices without affecting the meaning of the sentence, whereas, the words and the word order of speech transcriptions are strictly defined by the utterance. A confusion network is a linear graph where all paths travel via all nodes. There may be one or more word arcs between two consecutive nodes. These arcs may be viewed as alternative choices of words in a hypothesis. Thus, a confusion network may encode an exponential number of hypotheses. A word arc may also contain a NULL word which represents an empty word or a deletion. Fiscus (1997) aligns the STT outputs incrementally to form a confusion network. The vote count of each word arc is increased by one for each matching word in the alignment. The path with the highest total number of votes through the lattice defines the consensus output. Simple edit distance is sufficient in building confusion networks from STT system outputs since the outputs should follow a strict word order defined by the actual utterance. The most common STT quality metric, word error rate, only considers exact matches as correct. The order in which the STT system outputs are aligned does not significantly influence the resulting network. In machine translation, there may be several correct outputs with different word orders, as well as, different words or phrases with identical meaning. Two problems not relevant to combining STT system outputs arise: how to align outputs with different word orders and how to choose the word order of the final output. Many alignment algorithms for building confusion networks from MT system outputs have been proposed including edit distance based multiple string alignment (Bangalore et al. 2001), hidden Markov model based alignments (Matusov et al. Alignment algorithms based on TER approximations using both TERCOM and ITGs and symmetric word alignment from a hidden Markov Model (HMM) are detailed in this section. One MT hypothesis must be chosen to be the " skeleton " against …