Korean to English Translation Using Synchronous TAGs

It is often argued that accurate machine translation requires reference to contextual knowledge for the correct treatment of linguistic phenomena such as dropped arguments and accurate lexical selection. One of the historical arguments in favor of the interlingua approach has been that, since it revolves around a deep semantic representation, it is better able to handle the types of linguistic phenomena that are seen as requiring a knowledge-based approach. In this paper we present an alternative approach, exemplified by a prototype system for machine translation of English and Korean which is implemented in Synchronous TAGs. This approach is essentially transfer based, and uses semantic feature unification for accurate lexical selection of polysemous verbs. The same semantic features, when combined with a discourse model which stores previously mentioned entities, can also be used for the recovery of topicalized arguments. In this paper we concentrate on the translation of Korean to English.Comment: ps file. 8 page

An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

We present a web-based open-source tool for interactive translation prediction (ITP) and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.This work has been partly funded by the Spanish Ministerio de Economía y Competitividad through project TIN2012-32615

Η Αυτοματοποιημένη και μη-αυτοματοποιημένη αξιολόγηση συστήματος Στατιστικής Μηχανικής Μετάφρασης για το γλωσσικό ζεύγος Ελληνικά - Ιταλικά

Machine Translation (MT) evaluation is a hard task considering the difficulties that raise from the translation process itself. In this paper we present the results of the evaluation of a Statistical Machine Translation (SMT) system in which the Moses decoder was trained for the language pair Greek-Italian. The evaluation task was both automatic and non–automatic (human). For the automatic evaluation, the metrics BLEU, NIST were used, while for the human evaluation, the adequacy and the fluency of the translated texts was evaluated. A corpus of 120 individual sentences were evaluated, (e.g. EU texts, scientific technical texts, subtitles, proverbs etc.), by postgraduate students of the direction of Translation, Interpretation and Communication of the Department of Italian Language and Literature of the Aristotle University of Thessaloniki. The first results show that SMT performs well when translating text of this typ

A preliminary evaluation of metadata records machine translation

Article discussing a preliminary evaluation study of metadata records machine translation. This study evaluates freely available machine translation (MT) services' performance in translating metadata records

Korean Grammar Using TAGs

This paper addresses various issues related to representing the Korean language using Tree Adjoining Grammars. Topics covered include Korean grammar using TAGs, Machine Translation between Korean and English using Synchronous Tree Adjoining Grammars (STAGs), handling scrambling using Multi Component TAGs (MC-TAGs), and recovering empty arguments. The data for the parsing is from US military communication messages

Toward Multi-Engine Machine Translation

Current MT systems, whatever translation method they at present employ, do not reach an optimum output on free text. Our hy-pothesis for the experiment reported in this paper is that if an MT environment can use the best results from a variety of MT systems working simultaneously on the same text, the overallquality will im-prove. Using this novel approach to MT in the latest version of the Pangloss MT project, we submit an input text to a battery of machine translation systems (engines), coLlect their (possibly, incomplete) re-sults in a joint chaR-like data structure and select the overall best translation using a set of simple heuristics. This paper describes the simple mechanism we use for combining the findings of the various translation engines. 1