Miguel Menezes


pdf bib
Context-aware and gender-neutral Translation Memories
Marjolene Paulo | Vera Cabarrão | Helena Moniz | Miguel Menezes | Rachel Grewcock | Eduardo Farah
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

This work proposes an approach to use Part-Of-Speech (POS) information to automatically detect context-dependent Translation Units (TUs) from a Translation Memory database pertaining to the customer support domain. In line with our goal to minimize context-dependency in TUs, we show how this mechanism can be deployed to create new gender-neutral and context-independent TUs. Our experiments, conducted across Portuguese (PT), Brazilian Portuguese (PT-BR), Spanish (ES), and Spanish-Latam (ES-LATAM), show that the occurrence of certain POS with specific words is accurate in identifying context dependency. In a cross-client analysis, we found that ~10% of the most frequent 13,200 TUs were context-dependent, with gender determining context-dependency in 98% of all confirmed cases. We used these findings to suggest gender-neutral equivalents for the most frequent TUs with gender constraints. Our approach is in use in the Unbabel translation pipeline, and can be integrated into any other Neural Machine Translation (NMT) pipeline.

pdf bib
A Context-Aware Annotation Framework for Customer Support Live Chat Machine Translation
Miguel Menezes | M. Amin Farajian | Helena Moniz | João Varelas Graça
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

To measure context-aware machine translation (MT) systems quality, existing solutions have recommended human annotators to consider the full context of a document. In our work, we revised a well known Machine Translation quality assessment framework, Multidimensional Quality Metrics (MQM), (Lommel et al., 2014) by introducing a set of nine annotation categories that allows to map MT errors to source document contextual phenomenon, for simplicity sake we named such phenomena as contextual triggers. Our analysis shows that the adapted categories set enhanced MQM’s potential for MT error identification, being able to cover up to 61% more errors, when compared to traditional non-context core MQM’s application. Subsequently, we analyzed the severity of these MT “contextual errors”, showing that the majority fall under the critical and major levels, further indicating the impact of such errors. Finally, we measured the ability of existing evaluation metrics in detecting the proposed MT “contextual errors”. The results have shown that current state-of-the-art metrics fall short in detecting MT errors that are caused by contextual triggers on the source document side. With the work developed, we hope to understand how impactful context is for enhancing quality within a MT workflow and draw attention to future integration of the proposed contextual annotation framework into current MQM’s core typology.


pdf bib
A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content
Miguel Menezes | Vera Cabarrão | Pedro Mota | Helena Moniz | Alon Lavie
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This paper describes the research developed at Unbabel, a Portuguese Machine-translation start-up, that combines MT with human post-edition and focuses strictly on customer service content. We aim to contribute to furthering MT quality and good-practices by exposing the importance of having a continuously-in-development robust Named Entity Recognition system compliant with General Data Protection Regulation (GDPR). Moreover, we have tested semiautomatic strategies that support and enhance the creation of Named Entities gold standards to allow a more seamless implementation of Multilingual Named Entities Recognition Systems. The project described in this paper is the result of a shared work between Unbabel ́s linguists and Unbabel ́s AI engineering team, matured over a year. The project should, also, be taken as a statement of multidisciplinary, proving and validating the much-needed articulation between the different scientific fields that compose and characterize the area of Natural Language Processing (NLP).


pdf bib
DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
Sheila Castilho | João Lucas Cavalheiro Camargo | Miguel Menezes | Andy Way
Proceedings of the Sixth Conference on Machine Translation

Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of “human parity”, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind.