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Found 12 papers, 3 papers with code
A Reproduction Study of an Annotation-based Human Evaluation of MT Outputs
In this paper we report our reproduction study of the Croatian part of an annotation-based human evaluation of machine-translated user reviews (Popovic, 2020).
Leveraging Pre-trained Language Models for Gender Debiasing
This work explores a light-weight method to generate gender variants for a given text using pre-trained language models as the resource, without any task-specific labelled data.
On Machine Translation of User Reviews
This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages.
Neural Machine Translation between similar South-Slavic languages
This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation.
Neural Machine Translation for translating into Croatian and Serbian
Furthermore, translation performance from English is much better than from German, partly because German is morphologically more complex and partly because the corpus consists mostly of parallel human translations instead of original text and its human translation.
A General Framework for Detecting Metaphorical Collocations
This paper aims at identifying a specific set of collocations known under the term metaphorical collocations.
Agree to Disagree: Analysis of Inter-Annotator Disagreements in Human Evaluation of Machine Translation Output
This work describes an analysis of inter-annotator disagreements in human evaluation of machine translation output.
DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations
The resulting corpus consists of English news and reviews source texts, their translations into Russian and Croatian, and translations of the reviews into Finnish.
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
High-quality Machine Translation (MT) evaluation relies heavily on human judgments.
Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents
This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying.
Quantified Reproducibility Assessment of NLP Results
This paper describes and tests a method for carrying out quantified reproducibility assessment (QRA) that is based on concepts and definitions from metrology.
