Hierarchical Phrase-based Stream Decoding

This paper proposes a method for hierar-chical phrase-based stream decoding. A stream decoder is able to take a contin-uous stream of tokens as input, and seg-ments this stream into word sequences that are translated and output as a stream of tar-get word sequences. Phrase-based stream decoding techniques have been shown to be effective as a means of simultaneous in-terpretation. In this paper we transfer the essence of this idea into the framework of hierarchical machine translation. The hi-erarchical decoding framework organizes the decoding process into a chart; this structure is naturally suited to the process of stream decoding, leading to an efficient stream decoding algorithm that searches a restricted subspace containing only rel-evant hypotheses. Furthermore, the de-coder allows more explicit access to the word re-ordering process that is of crit-ical importance in decoding while inter-preting. The decoder was evaluated on TED talk data for English-Spanish and English-Chinese. Our results show that like the phrase-based stream decoder, the hierarchical is capable of approaching the performance of the underlying hierarchi-cal phrase-based machine translation de-coder, at useful levels of latency. In ad-dition the hierarchical approach appeared to be robust to the difficulties presented by the more challenging English-Chinese task.