| AL-SMT: Multilingual Setting | Our goal is to add a new language to this corpus, and at the same time to construct high quality MT systems from the existing languages (in the multilingual corpus) to the new language. |
| Abstract | We introduce an active learning task of adding a new language to an existing multilingual set of parallel text and constructing high quality MT systems , from each language in the collection into this new target language. |
| Introduction | In this paper, we consider how to use active learning (AL) in order to add a new language to such a multilingual parallel corpus and at the same time we construct an MT system from each language in the original corpus into this new target language. |
| Introduction | In this paper, we explore how multiple MT systems can be used to effectively pick instances that are more likely to improve training quality. |
| Introduction | When we build multiple MT systems from multiple source languages to the new target language, each MT system can be seen as a different ‘view’ on the desired output translation. |
| Abstract | We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Context Free Grammars. |
| Discussion | On hypergraphs produced by Hierarchical and Syntax Augmented MT systems , our MBR algorithm gives a 7X speedup relative to 1000-best MBR while giving comparable or even better performance. |
| Discussion | We believe that our efficient algorithms will make them more widely applicable in both SCFG—based and phrase-based MT systems . |
| Experiments | 6.2 MT System Description |
| Experiments | Our phrase-based statistical MT system is similar to the alignment template system described in (Och and Ney, 2004; Tromble et al., 2008). |
| Experiments | We also train two SCFG—based MT systems : a hierarchical phrase-based SMT (Chiang, 2007) system and a syntax augmented machine translation (SAMT) system using the approach described in Zollmann and Venugopal (2006). |
| Introduction | In this paper, we extend MERT and MBR decoding to work on hypergraphs produced by SCFG—based MT systems . |
| Minimum Bayes-Risk Decoding | MBR decoding for translation can be performed by reranking an N -best list of hypotheses generated by an MT system (Kumar and Byme, 2004). |
| Minimum Bayes-Risk Decoding | We next extend the Lattice MBR decoding algorithm (Algorithm 3) to rescore hypergraphs produced by a SCFG based MT system . |
| Methods 5.1 5W Systems | All three annotators were native English speakers who were not system developers for any of the SW systems that were being evaluated (to avoid biased grading, or assigning more blame to the MT system ). |
| Methods 5.1 5W Systems | If the SW system picked an incorrectly translated argument (e. g., “baked a moon” instead of “baked a cake”), then the error would be assigned to the MT system . |
| Results | Long-distance phrase movement is a common problem in Chinese-English MT, and many MT systems try to handle it (e. g., Wang et al. |
| Results | Since MT systems are tuned for word-based overlap measures (such as BLEU), verb deletion is penalized equally as, for example, determiner deletion. |
| SW System | In this section, we describe the individual systems that we evaluated, the combination strategy, the parsers that we tuned for the task, and the MT systems . |
| SW System | Finally, Chinese-align used the alignments of three separate MT systems to translate the 5Ws: a phrase-based system, a hierarchical phrase-based system, and a syntax augmented hierarchical phrase-based system. |
| SW System | Since the predicate is essential, it tried to detect when verbs were deleted in MT, and back-off to a different MT system . |
| Background 2.1 Terminology | It can be used to encode exponentially many hypotheses generated by a phrase-based MT system (e.g., Koehn et al. |
| Background 2.1 Terminology | (2003)) or a syntax-based MT system (e.g., Chiang (2007)). |
| Background 2.1 Terminology | To approximate the intractable decoding problem of (2), most MT systems (Koehn et al., 2003; Chiang, 2007) use a simple Viterbi approximation, |
| Introduction | They recover additional latent variables—so-called nuisance variables—that are not of interest to the user.1 For example, though machine translation (MT) seeks to output a string, typical MT systems (Koehn et al., 2003; Chiang, 2007) |
| Variational Approximate Decoding | For each input sentence c, we assume that a baseline MT system generates a hypergraph HG(cc) that compactly encodes the derivation set D(cc) along with a score for each d E D(9c),5 which we interpret as p(y, d | c) (or proportional to it). |
| Conclusion and Outlook | 2) and may find use in uncovering systematic shortcomings of MT systems . |
| Conclusion and Outlook | To some extent, of course, this problem holds as well for state-of—the-art MT systems . |
| EXpt. 1: Predicting Absolute Scores | Each language consists of 1500—2800 sentence pairs produced by 7—15 MT systems . |
| Introduction | Figure l: Entailment status between an MT system hypothesis and a reference translation for equivalent (top) and nonequivalent (bottom) translations. |