Experimenting the use of catenae in Phrase-Based SMT
Authors: Manuela Sanguinetti (Università di Torino, 2015)
Topic: Machine Translation

Abstract
Following recent trends on hybridization of machine translation architectures, this paper presents an experiment on the integration of a phrase-based system with syntactically-motivated bilingual pairs, namely the so-called catenae, extracted from a dependency-based parallel treebank. The experiment consisted in combining in different ways a phrase-based translation model, as typically conceived in Phrase-Based Statistical Machine Translation, with a small set of bilingual pairs of such catenae. The main goal is to study, though still in a preliminary fashion, how such units can be of any use in improving automatic translation quality.

Introduction
The hybridization of machine translation systems in order to benefit from both statistical-based and linguistically-motivated approaches is becoming a popular trend in translation field. Such trend is well described in a number of surveys (CostaJussá and Farrús, 2014; Costa-Jussá and Fonollosa, 2015) and witnessed by recent initiatives in NLP community, such as the HyTra workshop series1 . The motivations to this choice can be manifold, but essentially lie in the need to either reduce the costs - both in terms of time and resources - of building a fully rule-based system, or to integrate statistical models or SMT outputs with linguistic knowledge, as this could be useful to capture complex translation phenomena that data-driven approaches cannot handle properly. Such phenomena are often called translational divergences, or even shifts (Catford, 1965), and usually involve a large number of linguistic and extralinguistic factors. Our main research interest is the study of such shifts, in particular from a syntactic point of view, and of how such linguistic knowledge could be of any use to overcome the current shortcomings in machine translation. The preliminary experiment presented here is therefore guided by the second motivation mentioned above: our basic assumption is that supplementing translation models in classical PhraseBased Statistical Machine Translation (PBSMT) with syntactically-motivated units extracted from parallel treebanks can lead to improvements in machine translation accuracy. This was already demonstrated, for example, in Tinsley (2009), where syntactic contituents were used to improve the translation quality of a PBSMT system. However, instead of a constituency paradigm, we focused on a more dependency-oriented syntactic unit, namely the one of catena. The choice of a dependency-paradigm in general is mainly dictated by the acknowledged fact that dependencies can better represent linguistic phenomena typical of morphologically rich and free-word order languages (see e.g. (Covington, 1990; Goldberg et al., 2013)). On the other hand, to capture translation shifts of various nature, it is necessary to consider a syntactic unit that goes beyond the single node, as also recently pointed out, e.g., in Deng et al. (2015); hence the introduction of the notion of catena in our study. In order to verify our assumption, we carried out a preliminary experiment performing several translation tasks, with Italian and English as language pair. For this purpose, a typical phrase-based SMT system was built, using for training the translation model various combinations of baseline SMT configurations and pairs of catenae automatically extracted from a parallel treebank, i.e. ParTUT, and then automatically aligned. The remainder of this paper is thus organized as follows: Section 2 introduces the notion of catena, in Section 3 we describe our use of catenae in this experiment, while in Section 4 we describe the training configurations chosen and discuss the results.

Conclusion
The paper presented a small experiment on the combined use of linguistic knowledge - in the form of syntactically-motivated translation units - and statistical model provided by state-of-the-art machine translation techniques. The results reported here are to be considered preliminary, as they suffer from the absence of systematic procedures and data that could not have been applied so far due to lack of time and proper resources. Still, considering these shortcomings, translation evaluation, at least in one direction, produced promising results. There is however a lot of work to do to under this respect in order to effectively improve translation quality with the help of such linguistic information; for example by scaling up this experiment using a larger set of external data, or using different training configurations, so as to have multiple sources of comparison for final assessments and considerations.