The Seq2Seq model and its variants (ConvSeq2Seq Transformer) emerge as a promising novel solution to the machine translation problem. However, these models only focus on exploiting knowledge from bilingual sentences without paying much attention utilizing external linguistic sources such semantic representations. Not do representations can help preserve meaning but they also minimize data spars...

We introduce new features for incorporating semantic predicate-argument structures in machine translation (MT). The methods focus on the completeness of the semantic structures of the translations, as well as the order of the translated semantic roles. We experiment with translation rules which contain the core arguments for the predicates in the source side of a MT system, and observe that usi...

We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs judge the quality or the semantic similarity of text by using re...

A large number of machine translation approaches have been developed recently with the aim of migrating content easily across languages. However, the literature suggests that many obstacles must be dealt with to achieve better automatic translations. A central issue that machine translation systems must handle is ambiguity. A promising way of overcoming this problem is using semantic web techno...

A semantic feature for statistical machine translation, based on Latent Semantic Indexing, is proposed and evaluated. The objective of the proposed feature is to account for the degree of similarity between a given input sentence and each individual sentence in the training dataset. This similarity is computed in a reduced vectorspace constructed by means of the Latent Semantic Indexing decompo...

We present a translation model enriched with shallow syntactic and semantic information about the source language. Base-phrase labels and semantic role labels are incorporated into an hierarchical model by creating shallow semantic “trees”. Results show an increase in performance of up to 6% in BLEU scores for English-Spanish translation over a standard phrase-based SMT baseline.

Abstract The aim of this study was to find the appropriate type of translation of English brand names Iranian consumers prefer, i.e. phonetic, semantic and phonosemantic types of translation. Also, it was aimed at examining the type of font size Iranian consumers tends to see on packages of English products. To this end, the distinction between male and female participants as well as the partic...

This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a lowdimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural network whose weights are learned on parall...