30.66, Books: Quality Estimation for Machine Translation: Specia, Scarton, Paetzold

[The LINGUIST List]

LINGUIST List: Vol-30-66. Mon Jan 07 2019. ISSN: 1069 - 4875.

Subject: 30.66, Books: Quality Estimation for Machine Translation: Specia, Scarton, Paetzold

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Date: Mon, 07 Jan 2019 16:14:28
From: Bebe Barrow [barrow at morganclaypool.com]
Subject: Quality Estimation for Machine Translation: Specia, Scarton, Paetzold

 


Title: Quality Estimation for Machine Translation 
Series Title: Synthesis Lectures on Human Language Technologies edited by Graeme Hirst  

Publication Year: 2018 
Publisher: Morgan & Claypool Publishers
	   http://www.morganclaypool.com
	

Book URL: http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1309 


Author: Lucia Specia
Author: Carolina Scarton
Author: Gustavo Henrique Paetzold

Electronic: ISBN:  9781681733746 Pages: 162 Price: U.S. $ 51.96
Hardback: ISBN:  9781681733753 Pages: 162 Price: U.S. $ 84.95
Paperback: ISBN:  9781681733739 Pages: 162 Price: U.S. $ 64.95


Abstract:

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 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 the
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.
 



Linguistic Field(s): Computational Linguistics
                     Text/Corpus Linguistics
                     Translation


Written In: English  (eng)

See this book announcement on our website: 
http://linguistlist.org/pubs/books/get-book.cfm?BookID=132373




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