| 書(shū)目名稱(chēng) | Quality Estimation for Machine Translation | | 編輯 | Lucia Specia,Carolina Scarton,Gustavo Henrique Pae | | 視頻video | http://file.papertrans.cn/781/780265/780265.mp4 | | 叢書(shū)名稱(chēng) | Synthesis Lectures on Human Language Technologies | | 圖書(shū)封面 |  | | 描述 | Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used inproduction (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimati | | 出版日期 | Book 2018 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02168-8 | | isbn_softcover | 978-3-031-01040-8 | | isbn_ebook | 978-3-031-02168-8Series ISSN 1947-4040 Series E-ISSN 1947-4059 | | issn_series | 1947-4040 | | copyright | Springer Nature Switzerland AG 2018 |
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