The development of large-scale automatic classroom dialog analysis systems requires accurate speech-to-text translation. A variety of automatic speech recognition (ASR) engines were evaluated for this purpose. Recordings of teachers in noisy classrooms were used for testing. In comparing ASR results, Google Speech and Bing Speech were more accurate with word accuracy scores of 0.56 for Google a...

One of the LT-applications that ensures the access to the information, in the user’s mother tongue, is machine translation (MT). Unfortunately less spoken languages a category in which the Balkan and Slavic languages can be included have to overcome a major gap in language resources, reference-systems and tools. In its simplest form, statistical machine translation (SMT) is based only on the ex...

Recently, Machine Translation (MT) has become a quite popular technology in everyday use through Web services such as Google Translate. Although the different MT approaches provide good results, none of them exploits contextual information like Named Entity (NE) to help user comprehension. In this paper, we present NERITS, a machine translation mashup system using semantic annotation from Wikim...

This paper documents recent work carried out for PeEn-SMT, our Statistical Machine Translation system for translation between the English-Persian language pair. We give details of our previous SMT system, and present our current development of significantly larger corpora. We explain how recent tests using much larger corpora helped to evaluate problems in parallel corpus alignment, corpus cont...

In this paper we report a way of constructing a translation corpus that contains not only source and target texts, but draft and final versions of target texts, through the translation hosting site Minna no Hon’yaku (MNH). We made MNH publicly available on April 2009. Since then, more than 1,000 users have registered and over 3,500 documents have been translated, as of February 2010, from Engli...

Sequence to Sequence Neural Machine Translation has achieved significant performance in recent years. Yet, there are some existing issues that Neural Machine Translation still does not solve completely. Two of them are translation of long sentences and “over-translation”. To address these two problems, we propose an approach that utilize more grammatical information such as syntactic dependenci...

In today’s digital world automated Machine Translation of one language to another has covered a long way to achieve different kinds of success stories. Whereas Babel Fish supports a good number of foreign languages and only Hindi from Indian languages, the Google Translator takes care of about 10 Indian languages. Though most of the Automated Machine Translation Systems are doing well but handl...

In this paper we report a way of constructing a translation corpus that contains not only source and target texts, but draft and final versions of target texts, through the translation hosting site Minna no Hon’yaku (MNH). We made MNH publicly available on April 2009. Since then, more than 1,000 users have registered and over 3,500 documents have been translated, as of February 2010, from Engli...

The ever-increasing numbers of Web-accessible documents are available in languages other than English. The management of these heterogeneous document collections has posed a challenge. This paper proposes a novel model, called a domain alignment translation model, to conduct cross-lingual document clustering. While most existing crosslingual document clustering methods make use of an expensive ...