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003			ES-MaUEC
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006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031021688
024	7		_a10.1007/978-3-031-02168-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP308 _b2018 EB
100	1		_aSpecia, Lucia _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687410
245	1	0	_aQuality Estimation for Machine Translation _cby Lucia Specia, Carolina Scarton, Gustavo Henrique Paetzold
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIII, 148 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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 in production (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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9158290 _aInterlengua (Aprendizaje de lenguas)
650		7	_2embne _9140340 _aTraducción automática _xEvaluación
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
700	1		_aScarton, Carolina _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687411
700	1		_aPaetzold, Gustavo Henrique _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687412
776	0	8	_iPrinted edition: _z9783031001796
776	0	8	_iPrinted edition: _z9783031010408
776	0	8	_iPrinted edition: _z9783031032967
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02168-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI