We compare two pivot strategies for phrase-based statistical machine translation (SMT), namely phrase translation and sentence translation. The phrase translation strategy means that we directly construct a phrase translation table (phrase-table) of the source and target language pair from two phrase-tables; one constructed from the source language and English and one constructed from English a...

Many studies abroad have focused on neural machine translation and almost all concluded that this method was much closer to humanistic translation than machine translation. Therefore, this paper aimed at investigating whether neural machine translation was more acceptable in English-Persian translation in comparison with machine translation. Hence, two types of text were chosen to be translated...

Phrase-based machine translation models have shown to yield better translations than Word-based models, since phrase pairs encode the contextual information that is needed for a more accurate translation. However, many phrase pairs do not encode any relevant context, which means that the translation event encoded in that phrase pair is led by smaller translation events that are independent from...

In machine translation it is common phenomenon that machine-readable dictionaries and standard parsing rules are not enough to ensure accuracy in parsing and translating English phrases into Korean language, which is revealed in misleading translation results due to consequent structural and semantic ambiguities. This paper aims to suggest a solution to structural and semantic ambiguities due t...

Machine Translation (MT) is the task of automatically translating a text from one language to another. In this work we describe a phrase-based Statistical Machine Translation (SMT) system that translates English sentences to Bangla. A transliteration module is added to handle outof-vocabulary (OOV) words. This is especially useful for low-density languages like Bangla for which only a limited a...

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 presents the utilization of chunk phrases to facilitate evaluation of machine translation. Since most of current researches on evaluation take great effects to evaluate translation quality on content relevance and readability, we further introduce high-level abstract information such as semantic similarity and topic model into this phrase-based evaluation metric. The proposed metric ...

The aim of this thesis is to express fundamentals and concepts behind one of the emerging techniques in statistical machine translation (SMT) hierarchical phrase based MT by implementing translation from Hindi to English. Basically hierarchical model extends phrase based models by considering subphrases with the aid of context free grammar (CFG). In other models, syntax based models bear a rese...

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...

In this paper, we present Neural Phrase-based Machine Translation (NPMT).1 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 e...