Trustful Test Suites for Natural Language Processing

• Autor(a) principal: Cabeça, Mariana
• Data de Publicação: 2023
• Outros Autores: Buchicchio, Marianna, Moniz, Helena
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.26334/2183-9077/rapln10ano2023a4
• topic: Sistemas de Deteção Automática de Erros; Avaliação de desempenho; Corpus de teste; Avaliação de Sistemas de PLN; Grammar Error Detection; Performance assessment; Test Suites; NLP systems evaluation

Machine Translation (MT) research has witnessed continuous growth, accompanied by an increasing demand for automated error detection and correction in textual content. In response, Unbabel has developed a hybrid approach that combines machine translation with human editors in post-edition (PE) to provide high-quality translations. To facilitate the tasks of post-editors, Unbabel has created a proprietary error detection tool named Smartcheck, designed to identify errors and provide correction suggestions. Traditionally, the evaluation of translation errors relies on carefully curated annotated texts, categorized based on error types, which serve as the evaluation standard or Test Suites for assessing the accuracy of machine translation systems. However, it is crucial to consider that the effectiveness of evaluation sets can significantly impact the outcomes of evaluations. In fact, if evaluation sets do not accurately represent the content or possess inherent flaws, the decisions made based on such evaluations may inadvertently yield undesired effects. Hence, it is of utmost importance to employ suitable datasets containing representative data of the structures needed for each system, including Smartcheck. In this paper we present the methodology that has been developed and implemented to create reliable and revised Test Suites specifically designed for the evaluation process of MT systems and error detection tools. By using these meticulously curated Test Suites to evaluate proprietary systems and tools, we can ensure the trustworthiness of the conclusions and decisions derived from the evaluations. This methodology accomplished robust identification of problematic error types, grammar-checking rules, and language- and/or register-specific issues, leading to the adoption of effective production measures. With the integration of Smartcheck’s reliable and accurate correction suggestions and the improvements made to the post-edition revision process, the work presented herein led to a noticeable improvement in the translation quality delivered to customers.