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Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
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In: Front Artif Intell (2022)
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English WordNet Taxonomic Random Walk Pseudo-Corpora
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In: Conference papers (2020)
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Language related issues for machine translation between closely related south Slavic languages
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Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
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In: Conference papers (2019)
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Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
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In: Articles (2019)
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Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ...
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Is it worth it? Budget-related evaluation metrics for model selection ...
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Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
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In: Articles (2018)
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Is it worth it? Budget-related evaluation metrics for model selection
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In: Conference papers (2018)
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hr500k – A Reference Training Corpus of Croatian.
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In: Conference papers (2018)
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Fine-grained human evaluation of neural versus phrase-based machine translation ...
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Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017)
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Abstract:
We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems’ outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1515/pralin-2017-0014 https://doaj.org/article/b4e1fd45807c4747bcc465fbf853507b
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