Evaluating Common Sense Reasoning In Pretrained Transformer Based Language Models Using Adversarial Schemas And Consistency Metrics
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Evaluating Common Sense Reasoning In Pretrained Transformer Based Language Models Using Adversarial Schemas And Consistency Metrics
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Author : Adrian Maler
language : en
Publisher:
Release Date : 2023
Evaluating Common Sense Reasoning In Pretrained Transformer Based Language Models Using Adversarial Schemas And Consistency Metrics written by Adrian Maler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
In artificial intelligence, common sense refers to simple acts of verbal reasoning. The Winograd Schema Challenge (WSC), an important test of common sense, was recently defeated by transformer-based language models. We investigate the implications of that defeat: have language models achieved common sense, or is the challenge flawed? That is, we consider the problem of reevaluating verbal reasoning in language models. We evaluate the accuracy and consistency on Winograd schemas of three important pretrained models: GPT-2, RoBERTa, and T5. We generalize the Winograd schema to a larger class of problems, called adversarial schemas, and propose an evaluation protocol for them that incorporates consistency. We create a new test of common-sense verbal reasoning made up of our adversarial schemas. Each model performs significantly worse on our test than on WSC, and no model exhibits high consistency. We find no convincing evidence of verbal reasoning by language models.
Commonsense Reasoning With Discourse Relations
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Author : Prajjwal Bhargava
language : en
Publisher:
Release Date : 2022
Commonsense Reasoning With Discourse Relations written by Prajjwal Bhargava and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
To understand language, machines need to be cognizant of the implied commonsense knowledgeunderlying the text. Observations obtained from real-world scenarios contain richer and moredetailed information than what natural language typically conveys. In such cases, it is imperativefor language understanding systems to make accurate inferences about commonsense knowledgeto fill in what is being assumed. While performing commonsense reasoning over text is trivial formost humans, it remains an elusive goal for NLP and more broadly AI systems. Moreover, systemsthat struggle to perform commonsense reasoning in the desired manner risk producing redundantand even harmful outputs.To address the underlying challenges, it is necessary to understand how the task of commonsensereasoning has been dealt with pre-trained language models (PLMs), as well as the pitfalls andthe strengths of PLMs to discern what kind of reasoning they are capable of performing. Theclosing gap between PLMs and human baselines on existing benchmarks suggests that there is arequirement for a new one that can reliably address the well-known limitations of PLMs. Giventhe importance and lack of work in discourse phenomena with PLMs and how current trainingobjectives prove to be insufficient, we present a discourse relation-based benchmark that requiresPLMs to reason over discourse markers and context to make the task intricate from a linguisticand commonsense standpoint. We hope that this benchmark would allow for newer commonsensereasoning methods to be developed in addition to serving as a resource that proves to be difficultfor state-of-the-art PLMs.The first part of the thesis provides a comprehensive survey discussing the strengths and weaknesses of state-of-the-art pre-trained models for commonsense reasoning and generation. In thesecond part, we present DISCOSENSE, a novel benchmark for performing commonsense reasoning through understanding discourse relations. This benchmark addresses two major shortcomingswith Pre-trained Language Models (PLMs), degradation in the reasoning capability of PLMs asthe complexity of the task increases and difficulty in determining the most plausible ending whenmultiple scenarios are possible with a provided situation. We generate compelling distractors inDISCOSENSE using Conditional Adversarial Filtering, an extension of Adversarial Filtering thatemploys conditional text generation. We show that state-of-the-art models struggle to perform wellon this task, making this an ideal benchmark for next-generation commonsense reasoning systems.We believe that this benchmark would contribute toward improving next-generation NLP systemsfor commonsense reasoning tasks. These sections are then followed by a discussion of avenues forfuture research.
Dual Learning
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Author : Tao Qin
language : en
Publisher: Springer Nature
Release Date : 2020-11-13
Dual Learning written by Tao Qin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis,question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech synthesis and recognition, (visual) question answering and generation, image captioning and generation, and code summarization and generation. Offering a systematic and comprehensive overview of dual learning, this book enables interested researchers (both established and newcomers) and practitioners to gain a better understanding of the state of the art in the field. It also provides suggestions for further reading and tools to help readers advance the area. The book is divided into five parts. The first part gives a brief introduction to machine learning and deep learning. The second part introduces the algorithms based on the dual reconstruction principle using machine translation, image translation, speech processing and other NLP/CV tasks as the demo applications. It covers algorithms, such as dual semi-supervised learning, dual unsupervised learning and multi-agent dual learning. In the context of image translation, it introduces algorithms including CycleGAN, DualGAN, DiscoGAN cdGAN and more recent techniques/applications. The third part presents various work based on the probability principle, including dual supervised learning and dual inference based on the joint-probability principle and dual semi-supervised learning based on the marginal-probability principle. The fourth part reviews various theoretical studies on dual learning and discusses its connections to other learning paradigms. The fifth part provides a summary and suggests future research directions.
Statistical Significance Testing For Natural Language Processing
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Author : Rotem Dror
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Statistical Significance Testing For Natural Language Processing written by Rotem Dror and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
Can Knowledge Rich Sentences Help Language Models To Solve Common Sense Reasoning Problems
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Author : Ashok Prakash
language : en
Publisher:
Release Date : 2019
Can Knowledge Rich Sentences Help Language Models To Solve Common Sense Reasoning Problems written by Ashok Prakash and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Commonsense reasoning categories.
Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem. In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
Representation Learning For Natural Language Processing
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2020-07-03
Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Computers categories.
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Graph Representation Learning
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Author : William L. William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Recent Advances In Natural Language Processing
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Author : Ruslan Mitkov
language : en
Publisher: John Benjamins Publishing
Release Date : 1997-01-01
Recent Advances In Natural Language Processing written by Ruslan Mitkov and has been published by John Benjamins Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-01-01 with Language Arts & Disciplines categories.
This volume is based on contributions from the First International Conference on Recent Advances in Natural Language Processing (RANLP'95) held in Tzigov Chark, Bulgaria, 14-16 September 1995. This conference was one of the most important and competitively reviewed conferences in Natural Language Processing (NLP) for 1995 with submissions from more than 30 countries. Of the 48 papers presented at RANLP'95, the best (revised) papers have been selected for this book, in the hope that they reflect the most significant and promising trends (and latest successful results) in NLP. The book is organised thematically and the contributions are grouped according to the traditional topics found in NLP: morphology, syntax, grammars, parsing, semantics, discourse, grammars, generation, machine translation, corpus processing and multimedia. To help the reader find his/her way, the authors have prepared an extensive index which contains major terms used in NLP; an index of authors which lists the names of the authors and the page numbers of their paper(s); a list of figures; and a list of tables. This book will be of interest to researchers, lecturers and graduate students interested in Natural Language Processing and more specifically to those who work in Computational Linguistics, Corpus Linguistics and Machine Translation.
Evaluating Natural Language Processing Systems
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Author : Karen Sparck Jones
language : en
Publisher: Springer Science & Business Media
Release Date : 1995
Evaluating Natural Language Processing Systems written by Karen Sparck Jones and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
This book is about the patterns of connections between brain structures. It reviews progress on the analysis of neuroanatomical connection data and presents six different approaches to data analysis. The results of their application to data from cat and monkey cortex are explored. This volume sheds light on the organization of the brain that is specified by its wiring.
Semantic Relations Between Nominals
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Author : Vivi Nastase
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-04-01
Semantic Relations Between Nominals written by Vivi Nastase and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-01 with Computers categories.
People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.