35.1879, Books: Referring expression generation in context: Same (2024)

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LINGUIST List: Vol-35-1879. Thu Jun 27 2024. ISSN: 1069 - 4875.

Subject: 35.1879, Books: Referring expression generation in context: Same (2024)

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Date: 26-Jun-2024
From: Sebastian Nordhoff [sebastian.nordhoff at langsci-press.org]
Subject: Referring expression generation in context: Same (2024)


Title: Referring expression generation in context
Subtitle: Combining linguistic and computational approaches
Series Title: Topics at the Grammar-Discourse Interface
Publication Year: 2024
Publisher: Language Science Press
                http://langsci-press.org
Book URL: https://langsci-press.org/catalog/book/451

Author: Fahime Same
eBook: ISBN: 978-3-96110-471-0 Pages: 274 Price: Europe EURO 0
Abstract:

Reference production, often termed Referring Expression Generation
(REG) in computational linguistics, encompasses two distinct tasks:
(1) one-shot REG, and (2) REG-in-context. One-shot REG explores which
properties of a referent offer a unique description of it. In
contrast, REG-in-context asks which (anaphoric) referring expressions
are optimal at various points in discourse.

This book offers a series of in-depth studies of the REG-in-context
task. It thoroughly explores various aspects of the task such as
corpus selection, computational methods, feature analysis, and
evaluation techniques. The comparative study of different corpora
highlights the pivotal role of corpus choice in REG-in-context
research, emphasizing its influence on all subsequent model
development steps. An experimental analysis of various feature-based
machine learning models reveals that those with a concise set of
linguistically-informed features can rival models with more features.
Furthermore, this work highlights the importance of paragraph-related
concepts, an area underexplored in Natural Language Generation (NLG).
The book offers a thorough evaluation of different approaches to the
REG-in-context task (rule-based, feature-based, and neural
end-to-end), and demonstrates that well-crafted, non-neural models are
capable of matching or surpassing the performance of neural
REG-in-context models. In addition, the book delves into post-hoc
experiments, aimed at improving the explainability of both neural and
classical REG-in-context models. It also addresses other critical
topics, such as the limitations of accuracy-based evaluation metrics
and the essential role of human evaluation in NLG research.

These studies collectively advance our understanding of
REG-in-context. They highlight the importance of selecting appropriate
corpora and targeted features. They show the need for context-aware
modeling and the value of a comprehensive approach to model evaluation
and interpretation. This detailed analysis of REG-in-context paves the
way for developing more sophisticated, linguistically-informed, and
contextually appropriate NLG systems.

Linguistic Field(s): Computational Linguistics

Written In: English (eng)



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LINGUIST List: Vol-35-1879
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