Topic-based term translation models for statistical machine translation

نویسندگان

Deyi Xiong

Fandong Meng

Qun Liu

چکیده

Article history: Received 24 August 2014 Received in revised form 9 December 2015 Accepted 14 December 2015 Available online 18 December 2015

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A new model for persian multi-part words edition based on statistical machine translation

Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some s...

متن کامل

Topic-Based Dissimilarity and Sensitivity Models for Translation Rule Selection

Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with so...

متن کامل

Phrase Pair Rescoring With Term Weighting For Statistical Machine Translation

We propose to score phrase translation pairs for statistical machine translation using term weight based models. These models employ tf.idf to encode the weights of content and non-content words in phrase translation pairs. The translation probability is then modeled by similarity functions defined in a vector space. Two similarity functions are compared. Using these models in a statistical mac...

متن کامل

Using Features from Topic Models to Alleviate Over-Generation in Hierarchical Phrase-Based Translation

In hierarchical phrase-based translation systems, the grammars (SCFG rules) have over-generation problem because we can replace the non-terminalX with almost everything without knowing the syntactic or semantic role ofX . In this paper, we present an approach that uses topic models to learn the distributions for non-terminals in each SCFG rule, based on which we further derive static features f...

متن کامل