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020 _a9783031021688
_9978-3-031-02168-8
024 7 _a10.1007/978-3-031-02168-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSpecia, Lucia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980685
245 1 0 _aQuality Estimation for Machine Translation
_h[electronic resource] /
_cby Lucia Specia, Carolina Scarton, Gustavo Henrique Paetzold.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 148 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface -- Acknowledgments -- Introduction -- Quality Estimation for MT at Subsentence Level -- Quality Estimation for MT at Sentence Level -- Quality Estimation for MT at Document Level -- Quality Estimation for other Applications -- Final Remarks -- Bibliography -- Authors' Biographies.
520 _aMany 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 Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications,including text simplification, text summarization, grammatical error correction, and natural language generation.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aScarton, Carolina.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980686
700 1 _aPaetzold, Gustavo Henrique.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980687
710 2 _aSpringerLink (Online service)
_980688
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001796
776 0 8 _iPrinted edition:
_z9783031010408
776 0 8 _iPrinted edition:
_z9783031032967
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980689
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02168-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85014
_d85014