# name
VERTa: a linguistic approach to automatic machine translation evaluation

# metadata.dc.description.abstract[].value
[eng] Machine translation (MT) is directly linked to its evaluation in order to
both compare different MT system outputs and analyse system errors so that they
can be addressed and corrected. As a consequence, MT evaluation has become
increasingly important and popular in the last decade, leading to the development of MT evaluation metrics aiming at automatically assessing MT output. Most of these
metrics use reference translations in order to compare system output, and the most
well-known and widely spread work at lexical level. In this study we describe and
present a linguistically-motivated metric, VERTa, which aims at using and combining a wide variety of linguistic features at lexical, morphological, syntactic and
semantic level. Before designing and developing VERTa a qualitative linguistic
analysis of data was performed so as to identify the linguistic phenomena that an
MT metric must consider (Comelles et al. 2017). In the present study we introduce
VERTa’s design and architecture and we report the experiments performed in order
to develop the metric and to check the suitability and interaction of the linguistic
information used. The experiments carried out go beyond traditional correlation
scores and step towards a more qualitative approach based on linguistic analysis.
Finally, in order to check the validity of the metric, an evaluation has been conducted comparing the metric’s performance to that of other well-known state-of-theart MT metrics.

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Language Resources And Evaluation, 2019, vol. 53, p. 57-86

# metadata.dc.source[].value
Articles publicats en revistes (Llengües i Literatures Modernes i Estudis Anglesos)

# metadata.dc.title[].value
VERTa: a linguistic approach to automatic machine translation evaluation