Grammatical Machine Translation
نویسندگان
چکیده
We present an approach to statistical machine translation that combines ideas from phrase-based SMT and traditional grammar-based MT. Our system incorporates the concept of multi-word translation units into transfer of dependency structure snippets, and models and trains statistical components according to stateof-the-art SMT systems. Compliant with classical transfer-based MT, target dependency structure snippets are input to a grammar-based generator. An experimental evaluation shows that the incorporation of a grammar-based generator into an SMT framework provides improved grammaticality while achieving state-of-the-art quality on in-coverage examples, suggesting a possible hybrid framework.
منابع مشابه
Using Grammatical Roles to Improve Statistical Machine Translation
Statistical machine translation systems often struggle to preserve predicateargument structure. We present a new hierarchical machine translation model that explicitly captures the grammatical roles taken on by the words and phrases being translated (e.g., subject, object, and indirect object). Although existing hierarchical and syntax-based grammars can capture how many arguments a predicate t...
متن کاملGrammatical Error Correction
Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be ab...
متن کاملSemi-Automatic Evaluation of the Grammatical Coverage of Machine Translation Systems
In this paper we present a methodology for automating the evaluation of the grammatical coverage of machine translation (MT) systems. The methodology is based on the importance of unfolded grammatical structures, which represent the most basic syntactic pattern for a sentence in a given language. A database of unfolded grammatical structures is built to evaluate the parser of any NLP or MT syst...
متن کاملA Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation
In this paper we present a Neural Network (NN) architecture for detecting grammatical errors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word representations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting overall post-editing effort. Our result...
متن کاملDiscriminative Reranking for Grammatical Error Correction with Statistical Machine Translation
Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error correction methods that employ statistical machine translation (SMT) have been proposed in recent years. An SMT system generates candidates with scores for all candidates and selects the sentence with the highest score as the correction result. However, the 1-bes...
متن کاملSharing resources between free/open-source rule-based machine translation systems: Grammatical Framework and Apertium
In this paper, we describe two methods developed for sharing linguistic data between two free and open source rule based machine translation systems: Apertium, a shallow-transfer system; and Grammatical Framework (GF), which performs a deeper syntactic transfer. In the first method, we describe the conversion of lexical data from Apertium to GF, while in the second one we automatically extract ...
متن کامل