Predicting the Performance of Parsing with Referential Translation Machines
Referential translation machine (RTM) is a prediction engine used for predicting the performance of natural language processing tasks including parsing, machine translation, and semantic similarity pioneering language, task, and domain independence. RTM results for predicting the performance of pars...
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| Format: | Article |
| Language: | English |
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Sciendo
2016-10-01
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| Series: | Prague Bulletin of Mathematical Linguistics |
| Online Access: | https://doi.org/10.1515/pralin-2016-0010 |
| Summary: | Referential translation machine (RTM) is a prediction engine used for predicting the performance of natural language processing tasks including parsing, machine translation, and semantic similarity pioneering language, task, and domain independence. RTM results for predicting the performance of parsing (PPP) in out-of-domain or in-domain settings with different training sets and types of features present results independent of language or parser. RTM PPP models can be used without parsing using only text input and without any parser or language dependent information. Our results detail prediction performance, top selected features, and lower bound on the prediction error of PPP. |
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| ISSN: | 1804-0462 |