The merit of phrase-based statistical machine translation is often reduced by the complexity to construct it. In this paper, we address some issues in phrase-based statistical machine translation, namely: the size of the phrase translation table, the use of underlying translation model probability and the length of the phrase unit. We present Level-Of-Detail (LOD) approach, an agglomerative app...

Current state-of-the-art machine translation systems use a phrase-based scoring model for choosing among candidate translations in a target language, typically English. These models are deemed phrase-based because candidate sentence scores are in large part a product of phrase translation probabilities. These translation probabilities must be learned in some unsupervised manner from a pair of s...

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...

In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using SleepWAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from ex...

This paper is based on the work carried out in the framework of the Verbmobil project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches. In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is t...

Example-based machine translation was first proposed by the author at the International NATO Symposium on Artificial and Human Intelligence held in Lyon, France, October, 1981, and the paper was included in the book: Artificial and Human Intelligence, NorthHolland, 1984 [1]. In this paper I discussed the followings in the context of comparing example based translation with ordinary machine tran...

This paper presents the overview of statistical machine translation systems that BJTU-NLP developed for the NTCIR-9 Patent Machine Translation Task (NTCIR-9 PatentMT). We compared the performance between phrase-based translation model and factored translation model in our Patent SMT of Chinese to English and English to Japanese. Factored translation model was proposed as an extended phrase-base...