Pronoun Translation in English-French Machine Translation: An Analysis of Error Types

Pronouns are a long-standing challenge in machine translation. We present a study of the performance of a range of rule-based, statistical and neural MT systems on pronoun translation based on an extensive manual evaluation using the PROTEST test suite, which enables a fine-grained analysis of diffe...

Full description

Saved in:
Bibliographic Details
Main Authors Hardmeier, Christian, Guillou, Liane
Format Journal Article
LanguageEnglish
Published 30.08.2018
Subjects
Computer Science - Computation and Language
Online AccessGet full text

Cover

More Information
Summary:Pronouns are a long-standing challenge in machine translation. We present a study of the performance of a range of rule-based, statistical and neural MT systems on pronoun translation based on an extensive manual evaluation using the PROTEST test suite, which enables a fine-grained analysis of different pronoun types and sheds light on the difficulties of the task. We find that the rule-based approaches in our corpus perform poorly as a result of oversimplification, whereas SMT and early NMT systems exhibit significant shortcomings due to a lack of awareness of the functional and referential properties of pronouns. A recent Transformer-based NMT system with cross-sentence context shows very promising results on non-anaphoric pronouns and intra-sentential anaphora, but there is still considerable room for improvement in examples with cross-sentence dependencies.
DOI:10.48550/arxiv.1808.10196