A Smorgasbord of Features for Statistical Machine Translation
نویسندگان
چکیده
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.
منابع مشابه
A Smorgasbord of Features for Automatic MT Evaluation
This document describes the approach by the NLP Group at the Technical University of Catalonia (UPC-LSI), for the shared task on Automatic Evaluation of Machine Translation at the ACL 2008 Third SMT Workshop.
متن کاملA 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 کاملThe Correlation of Machine Translation Evaluation Metrics with Human Judgement on Persian Language
Machine Translation Evaluation Metrics (MTEMs) are the central core of Machine Translation (MT) engines as they are developed based on frequent evaluation. Although MTEMs are widespread today, their validity and quality for many languages is still under question. The aim of this research study was to examine the validity and assess the quality of MTEMs from Lexical Similarity set on machine tra...
متن کاملA Hybrid Machine Translation System Based on a Monotone Decoder
In this paper, a hybrid Machine Translation (MT) system is proposed by combining the result of a rule-based machine translation (RBMT) system with a statistical approach. The RBMT uses a set of linguistic rules for translation, which leads to better translation results in terms of word ordering and syntactic structure. On the other hand, SMT works better in lexical choice. Therefore, in our sys...
متن کاملتعیین مرز و نوع عبارات نحوی در متون فارسی
Text tokenization is the process of tokenizing text to meaningful tokens such as words, phrases, sentences, etc. Tokenization of syntactical phrases named as chunking is an important preprocessing needed in many applications such as machine translation information retrieval, text to speech, etc. In this paper chunking of Farsi texts is done using statistical and learning methods and the grammat...
متن کامل