Simple Syntactic and Morphological Processing Can Help English-Hindi Statistical Machine Translation

Simple Syntactic and Morphological Processing Can Help English-Hindi Statistical Machine Translation Ananthakrishnan Ramanathan, Pushpak Bhattacharyya Department of Computer Science and Engineering Indian Institute of Technology Powai, Mumbai-400076 India {anand,pb}@cse.iitb.ac.in Jayprasad Hegde, Ritesh M. Shah, Sasikumar M CDAC Mumbai (formerly NCST) Gulmohar Cross Road No. 9 Juhu, Mumbai-400049 India {jjhegde,ritesh,sasi} @cdacmumbai.in Abstract In this paper, we report our work on incor- porating syntactic and morphological infor- mation for English to Hindi statistical ma- chine translation. Two simple and compu- tationally inexpensive ideas have proven to be surprisingly effective: (i) reordering the English source sentence as per Hindi syntax, and (ii) using the suffixes of Hindi words. The former is done by applying simple trans- formation rules on the English parse tree. The latter, by using a simple suffix separa- tion program. With only a small amount of bilingual training data and limited tools for Hindi, we achieve reasonable performance and substantial improvements over the base- line phrase-based system. Our approach es- chews the use of parsing or other sophisti- cated linguistic tools for the target language (Hindi) making it a useful framework for statistical machine translation from English to Indian languages in general, since such tools are not widely available for Indian lan- guages currently. 1 Introduction Techniques for leveraging syntactic and morpholog- ical information for statistical machine translation (SMT) are receiving a fair amount of attention nowa- days. For SMT from English to Indian languages, these techniques are especially important for the fol- lowing three reasons: (i) Indian languages differ widely from English in terms of word-order; (ii) In- dian languages are morphologically quite rich; and (iii) large amounts of parallel corpora are not avail- able for these languages, though smaller amounts of text in specific domains (such as health, tourism, and agriculture) are now becoming accessible. It might therefore be expected that using syntactic and mor- phological information for English to Indian lan- guage SMT will prove highly beneficial in terms of achieving reasonable performance out of limited parallel corpora. However, the difficulty in this is that crucial tools, such as parsers and morphological analyzers, are not widely available for Indian lan- guages yet. In this paper, we present our work on incorporat- ing syntactic and morphological information for En- glish to Hindi SMT. Our approach, which eschews the use of parsing and other tools for Hindi, is two- pronged: 1. Incorporating syntactic information by com- bining phrase-based models with a set of struc- tural preprocessing rules on English 2. Incorporating morphological information by using a simple suffix separation program for Hindi, the likes of which can be created with limited effort for other Indian languages as well Significant improvements over the baseline phrase-based SMT system are obtained using our approach. Table 1 illustrates this with an example 1. Since only limited linguistic effort and tools are required for the target language, we believe that the framework we propose is suitable for SMT from En- glish to other Indian languages as well. 1This example is discussed further in section …

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