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 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.
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