Machine translation is the process of translating text from one natural language to other using computers. The process requires extreme intelligence and experience like a human being that a machine usually lacks. Availability of machine translators for translation from English to Dravidian language, Malayalam is on the low. A few corpus-based and non-corpus based approaches have been tried in p...

We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than n-best list or lattice rescoring as the neu...

Concomitant with the globalization of food culture, demand for the recipes of specialty dishes has been increasing. The recent growth in recipe sharing websites and food blogs has resulted in numerous recipe texts being available for diverse foods in various languages. However, little work has been done on machine translation of recipe texts. In this paper, we address the task of translating re...

Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel attentional NMT with source dependency representation to improve translation performa...

Despite SMT (Statistical Machine Translation) recently revolutionised MT for major language pairs, when addressing under-resourced and, to some extent, mildly-resourced languages, it still faces some difficulties such as the need of important quantities of parallel texts, the limited guaranty of the quality, etc. We thus speculate that RBMT (Rule Based Machine Translation) can fill the gap for ...

This paper describes“KYOTO”EBMT system that attended PatentMT task at NTCIR-9. When translating very different language pairs such as Japanese-English and ChineseEnglish, it is very important to handle sentences in tree structures to overcome the difference. Some works incorporate tree structures in some parts of whole translation process, but not all the way from model training (parallel sente...

The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with ...

Automatic word alignment plays a critical role in statistical machine translation. Unfortunately the relationship between alignment quality and statistical machine translation performance has not been well understood. In the recent literature the alignment task has frequently been decoupled from the translation task, and assumptions have been made about measuring alignment quality for machine t...

Automatic word alignment plays a critical role in statistical machine translation. Unfortunately, the relationship between alignment quality and statistical machine translation performance has not been well understood. In the recent literature, the alignment task has frequently been decoupled from the translation task and assumptions have been made about measuring alignment quality for machine ...

Machine translation is the task of automatically nding the translation of a source sentence in the target language. Statistical machine translation (SMT) use parallel corpora or bilingual paired corpora that are known to be translations of each other to nd a likely translation for a given source sentence based on the observed translations. The task of machine translation can be seen as an insta...