Following the idea of using distributed semantic representations to facilitate the computation of semantic similarity between translation equivalents, we propose a novel framework to learn bilingual distributed phrase representations for machine translation. We first induce vector representations for words in the source and target language respectively, in their own semantic space. These word v...

We propose a new algorithm to induce inversion transduction grammars, in which a crosslingual semantic frame based objective function is injected as confidence weighting in the early stages of statistical machine translation training. Unlike recent work on improving translation adequacy that uses a monolingual semantic frame based objective function to drive the tuning of loglinear mixture weig...

The present web is existing in the HTML and XML formats for persons to browse. Recently there is a trend towards the semantic web where the information can be can be processed and understood by agents. Most of the present research works focus on the translation from HTML to semantic web, but seldom on XML. In this paper, we design a method to translate XML to semantic web. It is known that onto...

Unknown words are one of the key factors which drastically impact the translation quality. Traditionally, nearly all the related research work focus on obtaining the translation of the unknown words in different ways. In this paper, we propose a new perspective to handle unknown words in statistical machine translation. Instead of trying great effort to find the translation of unknown words, th...

Semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance. Here we approach it as a straightforward machine translation task, and demonstrate that standard machine translation components can be adapted into a semantic parser. In experiments on the multilingual GeoQuery corpus we find that our parser is competitive with the state of the art...

Semantic parsing maps a sentence in natural language into a structured meaning representation. Previous studies show that semantic parsing with synchronous contextfree grammars (SCFGs) achieves favorable performance over most other alternatives. Motivated by the observation that the performance of semantic parsing with SCFGs is closely tied to the translation rules, this paper explores extendin...

Current neural machine translation (NMT) usually extracts a fixedlength semantic representation for source sentence, and then depends on this representation to generate corresponding target translation. In this paper, we proposed a pivot-based semantic splicing model (PBSSM) to obtain a semantic representation including more translation information for source sentence, thus improving the transl...

We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model effectively judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain spec...

The revised hierarchical model of bilingualism (e.g., Kroll & Stewart, 1994) assumes that second language (L2) words primarily access semantics through their first language (L1) translation equivalents. Consequently, backward translation from L2 to L1 should not imply semantic access but occurs through lexical wordform associations. However, recent research with Dutch-French bilinguals showed t...

The revised hierarchical model of bilingualism (e.g., Kroll & Stewart, 1994) assumes that second language (L2) words primarily access semantics through their first language (L1) translation equivalents. Consequently, backward translation from L2 to L1 should not imply semantic access but occurs through lexical wordform associations. However, recent research with Dutch-French bilinguals showed t...