When Discovering Modernism was first published, it shed new and welcome light on the birth of Modernism. This reissue of Menand's classic intellectual history of T.S. Eliot and the singular role he played in the rise of literary modernism features an updated Afterword by the author, as well as a detailed critical appraisal of the progression of Eliot's career as a poet and critic. The new Afterword was adapted from Menand's critically lauded essay on Eliot in The Cambridge History of Literary Criticism, Volume Seven: Modernism and the New Criticism. Menand shows how Eliot's early views on literary value and authenticity, and his later repudiation of those views, reflect the profound changes regarding the understanding of literature and its significance that occurred in the early part of the twentieth century. It will prove an eye-opening study for readers with an interest in the writings of T.S. Eliot and other luminaries of the Modernist era.

To uphold family honor and tradition, Sheetal Prasad is forced to forsake the man she loves and marry playboy millionaire Rakesh Dhanraj while the citizens of Raigun, India, watch in envy. On her wedding night, however, Sheetal quickly learns that the stranger she married is as cold as the marble floors of the Dhanraj mansion. Forced to smile at family members and cameras and pretend there's nothing wrong with her marriage, Sheetal begins to discover that the family she married into harbors secrets, lies and deceptions powerful enough to tear apart her world. With no one to rely on and no escape, Sheetal must ally with her husband in an attempt to protect her infant son from the tyranny of his family.sion.

TV conservationist David Bellamy brings you facts on how to live an ecologically-sound life and conserve the planet! New edition using recycled paper and a brown card cover.

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.

Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

New - over 50% new material and artwork. Exam practice - realistic exam-style tasks are featured throughout. Exam training - regular tips and hints. Balance - equal prominence is given to Papers 1-5. A new speaking section provides structured preparation for Paper 5. Authentic - texts are based on authentic sources. Language development - detailed grammar and vocabulary input and practice appropriate to this level. Structured revision - regular Overview sections, Vocabulary and Progress Tests, and an Exam Practice Workbook. Michigan ECPE Workbook - supplementary material for students preparing for the University of Michigan ECPE examination.