For many years MT systems and tools were used principally for the production of good-quality translations: either MT in combination with controlled (restricted) input and/or with human post-editing; or computer-based translation tools by translators. Since 1990 the situation has changed. Corporate use of MT with human assistance has continued to expand (particularly in the area of localisation)...

Computer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we p...

Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however that much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar↔En) and compare it against a standard phrase-ba...

Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct hidden Markov ...

This document is the translation to English of my undergraduate research thesis, which was originally written in Catalan. At that point, writing it in Catalan seemed like a good idea but, eventually, I realized it was a very bad idea. This translation has been done automatically using Google Translate, so do not expect Shakespeare’s English. Actually, it is quite terrible, but I am trying to ma...

HIT2 Lab participated in NTCIR 7 IR4QA task. In this task many topics consist of name entities, so Google translation was used to translate query terms because of its high performance on name entity translation. We use KL-divergence model to perform retrieval and Chinese character bigram as our indexing unit. Pseudo feedback was used trying to improve average precision. We achieved competitive ...

This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multilingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar ...