Incorporating source-language paraphrases into phrase-based SMT with confusion networks

To increase the model coverage, sourcelanguage paraphrases have been utilized to boost SMT system performance. Previous work showed that word lattices constructed from paraphrases are able to reduce out-ofvocabulary words and to express inputs in different ways for better translation quality. However, such a word-lattice-based method suffers from two problems: 1) path duplications in word lattices decrease the capacities for potential paraphrases; 2) lattice decoding in SMT dramatically increases the search space and results in poor time efficiency. Therefore, in this paper, we adopt word confusion networks as the input structure to carry source-language paraphrase information. Similar to previous work, we use word lattices to build word confusion networks for merging of duplicated paths and faster decoding. Experiments are carried out on small-, medium- and large-scale English– Chinese translation tasks, and we show that compared with the word-lattice-based method, the decoding time on three tasks is reduced significantly (up to 79%) while comparable translation quality is obtained on the largescale task

A three-pass system combination framework by combining multiple hypothesis alignment methods

So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy out performs the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set

Using TERp to augment the system combination for SMT

TER-Plus (TERp) is an extended TER evaluation metric incorporating morphology, synonymy and paraphrases. There are three new edit operations in TERp: Stem Matches, Synonym Matches and Phrase Substitutions (Para-phrases). In this paper, we propose a TERp-based augmented system combination in terms of the backbone selection and consensus decoding network. Combining the new properties\ud of the TERp, we also propose a two-pass decoding strategy for the lattice-based phrase-level confusion network(CN) to generate the final result. The experiments conducted on the NIST2008 Chinese-to-English test set show that our TERp-based augmented system combination framework achieves significant improvements in terms of BLEU and TERp scores compared to the state-of-the-art word-level system combination framework and a TER-based combination strategy

Joint Morphological and Syntactic Disambiguation

In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve