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 ...
Automatic evaluation of Machine Translation (MT) quality is essential to developing highquality MT systems. Various evaluation metrics have been proposed, and BLEU is now used as ...
Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhit...
The need for automated text evaluation is common to several AI disciplines. In this work, we explore the use of Machine Translation (MT) evaluation metrics for Textual Case Based R...
Ibrahim Adeyanju, Nirmalie Wiratunga, Robert Lothi...
Information retrieval systems are compared using evaluation metrics, with researchers commonly reporting results for simple metrics such as precision-at-10 or reciprocal rank toge...
William Webber, Alistair Moffat, Justin Zobel, Tet...
In this paper, we propose a novel framework for extractive summarization. Our framework allows the summarizer to adapt and improve itself. Experimental results show that our summa...
Some time in the future, some spelling error correction system will correct all the errors, and only the errors. We need evaluation metrics that will tell us when this has been ac...
We describe a dataset containing 16,000 translations produced by four machine translation systems and manually annotated for quality by professional translators. This dataset can ...
Many automatic evaluation metrics for machine translation (MT) rely on making comparisons to human translations, a resource that may not always be available. We present a method f...
We propose an automatic machine translation (MT) evaluation metric that calculates a similarity score (based on precision and recall) of a pair of sentences. Unlike most metrics, ...
Recent years have seen increasing interest in automatic metrics for the evaluation of generation systems. When a system can generate syntactic variation, automatic evaluation becom...