This paper proposes a method for hierarchical phrase-based stream decoding.A stream decoder is able to take a continuous stream of tokens as input, and segments this stream into word sequences that are translated and output as a stream of target word sequences.Phrase-based stream decoding techniques have been shown to be effective as a means of simultaneous interpretation.In this paper we transfer the essence of this idea into the framework of hierarchical machine translation.The hierarchical 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 relevant hypotheses.Furthermore, the decoder allows more explicit access to the word re-ordering process that is of critical importance in decoding while interpreting.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 hierarchical phrase-based machine translation decoder, at useful levels of latency.In addition the hierarchical approach appeared to be robust to the difficulties presented by the more challenging English-Chinese task.

Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment.However, these algorithms have a problem of over-fitting, leading to "garbage collector effects," where rare words tend to be erroneously aligned to untranslated words.This paper proposes a leave-one-out expectationmaximization algorithm for unsupervised word alignment to address this problem.The proposed method excludes information derived from the alignment of a sentence pair from the alignment models used to align it.This prevents erroneous alignments within a sentence pair from supporting themselves.Experimental results on Chinese-English and Japanese-English corpora show that the F 1 , precision and recall of alignment were consistently increased by 5.0% -17.2%, and BLEU scores of end-to-end translation were raised by 0.03 -1.30.The proposed method also outperformed l 0 -normalized GIZA++ and Kneser-Ney smoothed GIZA++.

Languages that have no explicit word delimiters often have to be segmented for statistical machine translation (SMT).This is commonly performed by automated segmenters trained on manually annotated corpora.However, the word segmentation (WS) schemes of these annotated corpora are handcrafted for general usage, and may not be suitable for SMT.An analysis was performed to test this hypothesis using a manually annotated word alignment (WA) corpus for Chinese-English SMT.An analysis revealed that 74.60% of the sentences in the WA corpus if segmented using an automated segmenter trained on the Penn Chinese Treebank (CTB) will contain conflicts with the gold WA annotations.We formulated an approach based on word splitting with reference to the annotated WA to alleviate these conflicts.Experimental results show that the refined WS reduced word alignment error rate by 6.82% and achieved the highest BLEU improvement (0.63 on average) on the Chinese-English open machine translation (OpenMT) corpora compared to related work.

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes on which the most widely used approach to multiclass classification--one-versus-rest--becomes intractable due to computational complexity. The top-down methods are usually adopted instead, but they are less accurate because of the so-called error-propagation problem in their classifying phase. To address this problem, this paper proposes a meta-top-down method that employs metaclassification to enhance the normal top-down classifying procedure. The proposed method is first analyzed theoretically on complexity and accuracy, and then applied to five real-world large-scale data sets. The experimental results indicate that the classification accuracy is largely improved, while the increased time costs are smaller than most of the existing approaches.