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Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and…
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A proposal to automatically build and maintain gazetteers for Named Entity Recognition by using Wikipedia

This paper describes a method to automatically create and maintain gazetteers for Named Entity Recognition (NER) based on the analysis of an on-line encyclopedia entries by using a noun hierarchy and optionally a PoS tagger.
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Automatic Extraction of Arabic Multiword Expressions

This paper investigates the automatic acquisition of Arabic Multiword Expressions by proposing three complementary approaches to extract MWEs from available data resources and measuring the quality and coverage of the output against gold standards.
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A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions

It is found that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems.
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A Set of Recommendations for Assessing Human-Machine Parity in Language Translation

It is shown that the professional human translations contained significantly fewer errors, and that perceived quality in human evaluation depends on the choice of raters, the availability of linguistic context, and the creation of reference translations.
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Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation

This work compares three approaches to statistical machine translation by performing a fine-grained manual evaluation via error annotation of the systems’ outputs by finding the best performing system that reduces the errors produced by the worst system by 54%.
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Translators’ perceptions of literary post-editing using statistical and neural machine translation

In the context of recent improvements in the quality of machine translation (MT) output and new use cases being found for that output, this article reports on an experiment using statistical and…
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Post-editing Effort of a Novel With Statistical and Neural Machine Translation

This first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators is conducted, and both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%.
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Quantitative fine-grained human evaluation of machine translation systems: a case study on English to Croatian

A quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems and shows that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%).
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Named Entity WordNet

This paper presents the automatic extension of Princeton WordNet with Named Entities (NEs) and explores different aspects of the methodology such as the treatment of polysemous terms, the identification of hyponyms within the Wikipedia categorization system, the Identification of Wikipedia articles which are NEs and the design of a NE repository compliant with the LMF ISO standard.
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