Syntactic Features for Evaluation of Machine Translation
نویسندگان
چکیده
Automatic evaluation of machine translation, based on computing n-gram similarity between system output and human reference translations, has revolutionized the development of MT systems. We explore the use of syntactic information, including constituent labels and head-modifier dependencies, in computing similarity between output and reference. Our results show that adding syntactic information to the evaluation metric improves both sentence-level and corpus-level correlation with human judgments.
منابع مشابه
تخمین اطمینان خروجی ترجمه ماشینی با استفاده از ویژگی های جدید ساختاری و محتوایی
Despite machine translation (MT) wide suc-cess over last years, this technology is still not able to exactly translate text so that except for some language pairs in certain domains, post editing its output may take longer time than human translation. Nevertheless by having an estimation of the output quality, users can manage imperfection of this tech-nology. It means we need to estimate the c...
متن کاملA Smorgasbord of Features for Statistical Machine Translation
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 a...
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 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 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