Translation systems are complex, and most metrics do little to pinpoint causes of error or isolate system differences. We use a simple technique to discover induction errors, which occur when good translations are absent from model search spaces. Our results show that a common pruning heuristic drastically increases induction error, and also strongly suggest that the search spaces of phrase-based and hierarchical phrase-based models are highly overlapping despite the well known structural differences. 1

We introduce a discriminatively trained, globally normalized, log-linear variant of the lexical translation models proposed by Brown et al. (1993). In our model, arbitrary, nonindependent features may be freely incorporated, thereby overcoming the inherent limitation of generative models, which require that features be sensitive to the conditional independencies of the generative process. However, unlike previous work on discriminative modeling of word alignment (which also permits the use of arbitrary features), the parameters in our models are learned from unannotated parallel sentences, rather than from supervised word alignments. Using a variety of intrinsic and extrinsic measures, including translation performance, we show our model yields better alignments than generative baselines in a number of language pairs. 1

Abstract This chapter has two main aims: (i) to present the state-of-the-art in Machine Translation (MT), namely Phrase-Based Statistical MT, together with the major competing paradigms used in MT research and development today; and (ii) to provide an overview of the MT research carried out by my team here at DCU, characterised here in terms of ‘hybrid MT’. In addition, we provide our views on the directions that MT research might take in the near future, and conclude the chapter with lists of further reading for the interested reader.

Phrase-Based Statistical Machine Translation (PB-SMT) is clearly the leading paradigm in the field today. Nevertheless—and this may come as some surprise to the PB-SMT community—most translators, and somewhat more surprisingly perhaps, many experienced MT protagonists, find the basic model extremely difficult to understand. The main aim of this paper, therefore, is to discuss why this might be the case. Our basic thesis is that proponents of PB-SMT do not seek to address any community other than their own, for they do not feel any need to do so. We will demonstrate that this was not always the case; on the contrary, when statistical models of translation were first presented, the language used to describe how such a model might work was very conciliatory, and inclusive. Over the next five years things changed considerably; once SMT achieved dominance particularly over the rule-based paradigm, it had established a position where it did not need to bring along the rest of the MT community with it, and in our view, this has largely pertained to this day. Having discussed these issues, we will provide three additional observations: firstly, we will discuss the role of automatic MT evaluation metrics when describing PB-SMT systems; secondly, we will comment on the recent syntactic embellishments of PB-SMT, noting especially that most of these contributions have come from researchers who have prior experience in fields other than statistical models of translation; and finally, we will briefly comment on the relationship between PB-SMT and other models of translation, suggesting that there are many gains to be had if the SMT community were to open up more to the other MT paradigms. 1

This paper describes the German-English translation system developed by the ARK research group at Carnegie Mellon University for the Sixth Workshop on Machine Translation (WMT11). We present the results of several modeling and training improvements to our core hierarchical phrase-based translation system, including: feature engineering to improve modeling of the derivation structure of translations; better handing of OOVs; and using development set translations into other languages to create additional pseudoreferences for training. 1

We present progress on Joshua, an opensource decoder for hierarchical and syntaxbased machine translation. The main focus is describing Thrax, a flexible, open source synchronous context-free grammar extractor. Thrax extracts both hierarchical (Chiang, 2007) and syntax-augmented machine translation (Zollmann and Venugopal, 2006) grammars. It is built on Apache Hadoop for efficient distributed performance, and can easily be extended with support for new grammars, feature functions, and output formats. 1