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OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition or context information of OOV terms. Furthermore, non-existing methods focus on cross-language definition retrieval for OOV terms. Never the less, it has always been so difficult to...
This paper proposes neural networks approaches to recognize logical parts in Vietnamese legal documents. We utilize four models based on recurrent neural networks including Long Short Term Memory (LSTM), Bidirectional LSTM and their combination with Conditional Random Fields. The experimental results on the Vietnamese Business Law data set shows the promising of this approach. Although, these approaches...
Translating legal text is generally considered to be difficult because legal text has some characteristics that make it different from other daily-use documents and legal text is usually long and complicated. In order boost the legal text translation quality, splitting an input sentence becomes mandatory. In this paper, we propose a novel method based on the logical structure of legal text sentence...
Translation quality is often disappointed when a phrase based machine translation system deals with long sentences. Because of syntactic structure discrepancy between two languages, the translation output will not preserve the same word order as the source. When a sentence is long, it should be partitioned into several clauses and the word reordering in the translation should be done within clauses,...
This paper presents an approach to select appropriate translation rules to improve phrase-reordering of tree-based statistical machine translation. We propose new features with rich linguistic and contextual information. We give a new algorithm to extract features, use maximum entropy to combine rich linguistic and contextual information and integrate these features into the tree-based SMT model (Moses-chart)...
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