Semantic Roles for String to Tree Machine Translation
نویسندگان
چکیده
We experiment with adding semantic role information to a string-to-tree machine translation system based on the rule extraction procedure of Galley et al. (2004). We compare methods based on augmenting the set of nonterminals by adding semantic role labels, and altering the rule extraction process to produce a separate set of rules for each predicate that encompass its entire predicate-argument structure. Our results demonstrate that the second approach is effective in increasing the quality of translations.
منابع مشابه
Semantic Role Features for Machine Translation
We propose semantic role features for a Tree-to-String transducer to model the reordering/deletion of source-side semantic roles. These semantic features, as well as the Tree-to-String templates, are trained based on a conditional log-linear model and are shown to significantly outperform systems trained based on Max-Likelihood and EM. We also show significant improvement in sentence fluency by...
متن کاملCan Semantic Roles Improve Syntax-Based Machine Translation?
This paper compares the performance of a Tree-to-string (TTS) transducer with automatically generated/gold-standard parse trees and semantic roles. Experimental results show that improving the parsing quality can lead to significant improvement in MT performance and adding semantic roles in the syntax tree labels does not improve the TTS transducer. Another approach of using semantic roles: ske...
متن کاملComparing Representations of Semantic Roles for String-To-Tree Decoding
We introduce new features for incorporating semantic predicate-argument structures in machine translation (MT). The methods focus on the completeness of the semantic structures of the translations, as well as the order of the translated semantic roles. We experiment with translation rules which contain the core arguments for the predicates in the source side of a MT system, and observe that usi...
متن کاملبرچسبزنی خودکار نقشهای معنایی در جملات فارسی به کمک درختهای وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملFine-Grained Tree-to-String Translation Rule Extraction
Tree-to-string translation rules are widely used in linguistically syntax-based statistical machine translation systems. In this paper, we propose to use deep syntactic information for obtaining fine-grained translation rules. A head-driven phrase structure grammar (HPSG) parser is used to obtain the deep syntactic information, which includes a fine-grained description of the syntactic property...
متن کامل