tailieunhanh - Báo cáo khoa học: "The Contribution of Linguistic Features to Automatic Machine Translation Evaluation"

A number of approaches to Automatic MT Evaluation based on deep linguistic knowledge have been suggested. However, n-gram based metrics are still today the dominant approach. The main reason is that the advantages of employing deeper linguistic information have not been clarified yet. In this work, we propose a novel approach for meta-evaluation of MT evaluation metrics, since correlation cofficient against human judges do not reveal details about the advantages and disadvantages of particular metrics. . | The Contribution of Linguistic Features to Automatic Machine Translation Evaluation Enrique Amigo1 Jesus Gimenez2 Julio Gonzalo 1 Felisa Verdejo1 1UNED Madrid enrique julio felisa @ 2UPC Barcelona jgimenez@ Abstract A number of approaches to Automatic MT Evaluation based on deep linguistic knowledge have been suggested. However n-gram based metrics are still today the dominant approach. The main reason is that the advantages of employing deeper linguistic information have not been clarified yet. In this work we propose a novel approach for meta-evaluation of MT evaluation metrics since correlation cofficient against human judges do not reveal details about the advantages and disadvantages of particular metrics. We then use this approach to investigate the benefits of introducing linguistic features into evaluation metrics. Overall our experiments show that i both lexical and linguistic metrics present complementary advantages and ii combining both kinds of metrics yields the most robust metaevaluation performance. 1 Introduction Automatic evaluation methods based on similarity to human references have substantially accelerated the development cycle of many NLP tasks such as Machine Translation Automatic Summarization Sentence Compression and Language Generation. These automatic evaluation metrics allow developers to optimize their systems without the need for expensive human assessments for each of their possible system configurations. However estimating the system output quality according to its similarity to human references is not a trivial task. The main problem is that many NLP tasks are open subjective therefore different humans may generate different outputs all of them equally valid. Thus language variability is an issue. In order to tackle language variability in the context of Machine Translation a considerable effort has also been made to include deeper linguistic information in automatic evaluation metrics both syntactic and .

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