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Author: Robert Dale Publisher: Bradford Book ISBN: Category : Computers Languages : en Pages : 304
Book Description
Robert Dale presents a detailed description of the development of algorithms for the generation of referring expressions, and of the underlying structures that motivate these algorithms, in a dynamic domain. He provides a number of novel results in both knowledge representation and natural language generation that should have straightforward applications in other domains. Dale describes EPICURE, a natural language generating system, and its capacity to create referring expressions in a domain embodying several interesting features: The entities in the domain consist of masses and sets as well as the more usual singular individuals; during the development of a discourse, the entities may take on new properties, existing entities may be destroyed, and new entities may be created; and the discourses within which the entities appear are hierarchically structured, allowing for the integration of discourse-structural constraints on the use of anaphoric expressions. EPICURE is designed to generate text from underlying plans. Dale uses cooking recipes as examples, showing how the system must determine what level of explanation is required and how the events in the plan must be modeled to ensure that the references generated are accurate.
Author: Robert Dale Publisher: Bradford Book ISBN: Category : Computers Languages : en Pages : 304
Book Description
Robert Dale presents a detailed description of the development of algorithms for the generation of referring expressions, and of the underlying structures that motivate these algorithms, in a dynamic domain. He provides a number of novel results in both knowledge representation and natural language generation that should have straightforward applications in other domains. Dale describes EPICURE, a natural language generating system, and its capacity to create referring expressions in a domain embodying several interesting features: The entities in the domain consist of masses and sets as well as the more usual singular individuals; during the development of a discourse, the entities may take on new properties, existing entities may be destroyed, and new entities may be created; and the discourses within which the entities appear are hierarchically structured, allowing for the integration of discourse-structural constraints on the use of anaphoric expressions. EPICURE is designed to generate text from underlying plans. Dale uses cooking recipes as examples, showing how the system must determine what level of explanation is required and how the events in the plan must be modeled to ensure that the references generated are accurate.
Author: Fahime Same Publisher: Language Science Press ISBN: 3961104719 Category : Language Arts & Disciplines Languages : en Pages : 276
Book Description
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.
Author: Céline Jost Publisher: Springer Nature ISBN: 3030423077 Category : Social Science Languages : en Pages : 418
Book Description
This book offers the first comprehensive yet critical overview of methods used to evaluate interaction between humans and social robots. It reviews commonly used evaluation methods, and shows that they are not always suitable for this purpose. Using representative case studies, the book identifies good and bad practices for evaluating human-robot interactions and proposes new standardized processes as well as recommendations, carefully developed on the basis of intensive discussions between specialists in various HRI-related disciplines, e.g. psychology, ethology, ergonomics, sociology, ethnography, robotics, and computer science. The book is the result of a close, long-standing collaboration between the editors and the invited contributors, including, but not limited to, their inspiring discussions at the workshop on Evaluation Methods Standardization for Human-Robot Interaction (EMSHRI), which have been organized yearly since 2015. By highlighting and weighing good and bad practices in evaluation design for HRI, the book will stimulate the scientific community to search for better solutions, take advantages of interdisciplinary collaborations, and encourage the development of new standards to accommodate the growing presence of robots in the day-to-day and social lives of human beings.
Author: Fahime Same Publisher: BoD – Books on Demand ISBN: 3985541000 Category : Language Arts & Disciplines Languages : en Pages : 278
Book Description
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.
Author: Dan Schulman Publisher: ISBN: Category : Languages : en Pages : 72
Book Description
In this work we tackle the tasks of recognizing and generating natural language referring expressions in images. In the first task, given an image with several objects and an expression referring to a specific object within the image, we would like to recognize this object by retrieving a bounding box enclosing it. In the second task, given an image and a bounding box enclosing a specific object, we would like to generate an unambiguous expression describing the object. The datasets for these tasks are built using an interactive game called ReferIt (Kazemzadeh et al., 2014). In this work, we focus on two of the datasets in this family, RefCOCO and RefClef. While RefClef contains a variety of object types including background such as sky and clouds, which are hard to detect, RefCOCO contains mainly day-to-day objects, such as persons, cars and cats, which are easier to detect. Recent works focus mainly on RefCOCO variants and tend to ignore RefClef. The general pipeline for these tasks adopted by most approaches consists of the following steps: (1) Retrieve candidate object bounding boxes, either by using the gold-truth from the dataset, or by generating candidates using methods such as EdgeBox (Zitnick and Dollar, 2014) and MaskRCNN (He et al., 2017); (2) Encode both the entire image and each of the candidates using a Convolutional-Neural-Network (CNN); (3) Encode the referring expression using word embeddings and a Recurrent-Neural-Network (RNN); (4) Compute a score for each candidate based on the previous encodings and choose the highest scoring bounding-box. We reproduce state of the art models on the two tasks of natural language referring expressions recognition and generation in images and introduce two specific improvements: We show an automated way to augment an existing dataset, multiplying its size by two. We also show that by replacing the existing visual encoder component with a better one, we achieve better results. We compare our results with the current state-of-the-art in both tasks. On the basis of our experiments and dataset augmentation method, we investigate the differences between the existing datasets. We identify that segmentation methods that are aware of the semantic labels for the objects, such as MaskRCNN, provide better results on RefCOCO, while a more generalist method such as EdgeBox is more robust on the more challenging RefClef dataset. We also identify subsets of the dataset which are a priori more challenging for the family of neural architectures we have identified - because they lack a form of symmetry which simplifies the task. -- abstract.
Author: Ehud Reiter Publisher: Cambridge University Press ISBN: 0521620368 Category : Computers Languages : en Pages : 274
Book Description
This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.
Author: Robert Dale
Author: Robert Dale
Author: Fahime Same
Author: Céline Jost
Author: 
Author: Fahime Same
Author: Kate Scott
Author: Margaret A. Mitchell
Author: Dan Schulman
Author: Robert Dale
Author: Ehud Reiter