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DeepLo@EMNLP-IJCNLP 2019 - proceedings of the 2nd workshop on Deep Learning Approaches for Low-Resource natural language processing - proceedings

DeepLo@EMNLP-IJCNLP 2019 - Proceedings of the 2nd Workshop o...

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2nd workshop on Deep Learning Approaches for Low-Resource natural language processing, DeepLo@EMNLP-IJCNLP 2019

ISBN: (纸本)9781950737789

The proceedings contain 32 papers. The topics discussed include: a comparative analysis of unsupervised language adaptation methods;a logical-based corpus for cross-lingual evaluation;bad form: comparing context-based and form-based few-shot learning in distributional semantic models;bag-of-words transfer: non-contextual techniques for multi-task learning;BERT is not an interlingua and the bias of tokenization;cross-lingual joint entity and word embedding to improve entity linking and parallel sentence mining;deep bidirectional transformers for relation extraction without supervision;domain adaptation with BERT-based domain classification and data selection;and X-WikiRE: a large, multilingual resource for relation extraction as machine comprehension.

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The Sixth International workshop on Deep Learning on graphs - methods and Applications (DLG-KDD'21) 21

The Sixth International Workshop on Deep Learning on Graphs ...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Wu, Lingfei Tang, Jiliang Xia, Yinglong Pei, Jian Guo, Xiaojie Michigan State Univ E Lansing MI 48824 USA Facebook Menlo Pk CA USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450383325

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on graphs: Method and Applications (DLG-KDD'21)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.

关键词： graph neural network deep learning graph representation learning graph mining

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Leveraging language Models for Code Comment Classification 15

Leveraging Language Models for Code Comment Classification

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15th Forum for Information Retrieval Evaluation, FIRE 2023

作者： Patel, Jagrat Kadi Sarva Vishwavidyalaya LDRP-ITR Sector-15 KH-5 Gandhinagar382015 India

In the realm of software engineering, collaborative efforts among development teams are essential, and comments play a crucial role in maintaining and enhancing software quality. These comments serve various purposes, from clarifying complex code logic to aiding in debugging and providing insights into design decisions. However, distinguishing between useful and redundant comments can be a challenging task. This paper explores the use of language Model-based (LLM) approaches, specifically advanced models like GPT-3, to automate comment classification and assess their utility. By harnessing the contextual understanding and generation capabilities of these models, the research hypothesizes significant improvements in comment analysis. Extensive experiments using both real-world human-labeled comments and synthetic comments generated by ChatGPT demonstrate that LLMs can classify comments with remarkable accuracy, surpassing previous methods reliant on surface features. Additionally, the study critically examines factors such as pre-training data, comment coverage, and model architectures, shedding light on their impact on comment analysis. In summary, this research makes several notable contributions. It thoroughly explores the use of state-of-the-art LLMs for comment classification across diverse settings, provides a benchmark dataset of human-annotated comments, and shows that LLMs can substantially enhance codebase documentation by automatically identifying low-quality comments. These techniques have the potential to be integrated into Integrated Development Environments (IDEs) to offer developers continuous feedback. Finally, the paper opens up new possibilities for leveraging advanced natural language processing (NLP) in software engineering tasks that demand deeper code comprehension, despite lingering questions about model robustness and the nature of human-AI collaboration. This work highlights the immense potential of LLMs in transforming programming by master

关键词： Classification (of information)

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Enhancing Code Comment Classification Using language Models 15

Enhancing Code Comment Classification Using Language Models

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15th Forum for Information Retrieval Evaluation, FIRE 2023

作者： Barot, Jaivin Kadi Sarva Vishwavidyalaya LDRP Campus Sector-15 KH-5 Gandhinagar382015 India

In the realm of software development, collaboration among development teams is vital, and comments play a pivotal role in maintaining and improving software quality. Comments serve diverse purposes, from elucidating complex code logic to aiding in debugging and offering insights into design decisions. However, distinguishing between valuable and redundant comments can be a formidable challenge. This paper explores the potential of language Model-based (LLM) approaches, particularly advanced models such as GPT-3, to automate the classification of comments and evaluate their effectiveness. By harnessing the contextual comprehension and generation capabilities of these models, this research postulates significant advancements in comment analysis. Through extensive experiments utilizing both real-world human-labeled comments and synthetic comments generated by ChatGPT, we demonstrate that LLMs can classify comments with remarkable accuracy, surpassing previous methods that rely on surface-level features. Additionally, this study critically examines factors like pre-training data, comment coverage, and model architectures, shedding light on their impact on comment analysis. In summary, this research makes several substantial contributions. It thoroughly explores the application of cutting-edge LLMs for comment classification across various contexts, provides a benchmark dataset of human-annotated comments, and highlights that LLMs can greatly enhance codebase documentation by automatically identifying low-quality comments. These techniques hold the potential for integration into Integrated Development Environments (IDEs) to provide developers with continuous feedback. Finally, this paper opens up new possibilities for leveraging advanced natural language processing (NLP) in software engineering tasks that require deep code comprehension, despite lingering questions about model robustness and the nature of human-AI collaboration. This work underscores the enormous potenti

关键词： Classification (of information)

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Making Sense of Nonsense: Integrated Gradient-based Input Reduction to Improve Recall for Check-worthy Claim Detection 5

Making Sense of Nonsense: Integrated Gradient-based Input Re...

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5th Symposium of the Norwegian AI Society, NAIS 2023

作者： Sheikhi, Ghazaal Opdahl, Andreas L. Touileb, Samia Setty, Vinay University of Bergen Norway CHECK24 GmbH Germany University of Stavanger Norway

Analysing long text documents of political discourse to identify check-worthy claims (claim detection) is known to be an important task in automated fact-checking systems, as it saves the precious time of fact-checkers, allowing for more fact-checks. However, existing methods use black-box deep neural NLP models to detect check-worthy claims, which limits the understanding of the model and the mistakes they make. The aim of this study is therefore to leverage an explainable neural NLP method to improve the claim detection task. Specifically, we exploit well known integrated gradient-based input reduction on textCNN and BiLSTM to create two different reduced claim data sets from ClaimBuster. We observe that a higher recall in check-worthy claim detection is achieved on the data reduced by BiLSTM compared to the models trained on claims. This is an important remark since the cost of overlooking check-worthy claims is high in claim detection for fact-checking. This is also the case when a pre-trained BERT sequence classification model is fine-tuned on the reduced data set. We argue that removing superfluous tokens using explainable NLP could unlock the true potential of neural language models for claim detection, even though the reduced claims might make no sense to humans. Our findings provide insights on task formulation, design of annotation schema and data set preparation for check-worthy claim detection. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： natural language processing systems

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Towards Theory-based Moral AI: Moral AI with Aggregating Models based on Normative Ethical Theory

Towards Theory-based Moral AI: Moral AI with Aggregating Mod...

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2023 workshop on Ethics and Trust in Human-AI Collaboration: Socio-Technical Approaches, ETHAICS 2023

作者： Takeshita, Masashi Rzepka, Rafal Araki, Kenji Graduate School of Information Science and Technology Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan

Moral AI has been studied in the fields of philosophy and artificial intelligence. Although most existing studies are only theoretical, recent developments in AI have made it increasingly necessary to implement AI with morality. On the other hand, humans are under the moral uncertainty of not knowing what is morally right. In this paper, we implement the Maximizing Expected Choiceworthiness (MEC) algorithm, which aggregates outputs of models based on three normative theories of normative ethics to generate the most appropriate output. MEC is a method for making appropriate moral judgments under moral uncertainty. Our experimental results suggest that the output of MEC correlates to some extent with commonsense morality and that MEC can produce equally or more appropriate output than existing methods. © 2022 CEUR-WS. All rights reserved.

关键词： natural language processing systems

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Decomposing natural Logic Inferences for Neural NLI 5

Decomposing Natural Logic Inferences for Neural NLI

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5th workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Rozanova, Julia Ferreira, Deborah Thayaparan, Mokanarangan Valentino, Marco Freitas, André Department of Computer Science University of Manchester United Kingdom Idiap Research Institute Switzerland

ISBN: (纸本)9781959429050

In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion. Correctly identifying valid inferences in downward-monotone contexts is a known stumbling block for NLI performance, subsuming linguistic phenomena such as negation scope and generalized quantifiers. To understand this difficulty, we emphasize monotonicity as a property of a context and examine the extent to which models capture relevant monotonicity information in the vector representations which are intermediate to their decision making process. Drawing on the recent advancement of the probing paradigm, we compare the presence of monotonicity features across various models. We find that monotonicity information is notably weak in the representations of popular NLI models which achieve high scores on benchmarks, and observe that previous improvements to these models based on fine-tuning strategies have introduced stronger monotonicity features together with their improved performance on challenge sets. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents 3

DSC-IITISM at FinCausal 2021: Combining POS tagging with Att...

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3rd Financial Narrative processing workshop, FNP 2021

作者： Haldar, Gunjan Mittal, Aman Gupta, Pradyumna Dhanbad826004 India Dhanbad826004 India

Causality detection draws plenty of attention in the field of natural language processing and linguistics research. It has essential applications in information retrieval, event prediction, question answering, financial analysis, and market research. In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers. For this purpose, we propose an approach that combines POS tagging with the BIO scheme, which can be integrated with modern transformer models to address this challenge of identifying causality in a given text. Our best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of 0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal 2021 workshop. © proceedings of the 3rd Financial Narrative processing workshop, FNP 2021. All rights reserved.

关键词： natural language processing systems

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natural language processing 4 All (NLP4All): A New Online Platform for Teaching and Learning NLP Concepts 5

Natural Language Processing 4 All (NLP4All): A New Online Pl...

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5th workshop on Teaching natural language processing, Teaching NLP 2021

作者： Baglini, Rebekah Brita Hjorth, Hermes Arthur Aarhus University Interacting Minds Center Denmark Aarhus University ScienceAtHome Denmark

ISBN: (纸本)9781954085367

natural language processing offers new insights into language data across almost all disciplines and domains, and allows us to corroborate and/or challenge existing knowledge. The primary hurdles to widening participation in and use of these new research tools are, first, a lack of coding skills in students across K-16, and in the population at large, and second, a lack of knowledge of how NLP-methods can be used to answer questions of disciplinary interest outside of linguistics and/or computer science. To broaden participation in NLP and improve NLP-literacy, we introduced a new tool web-based tool called natural language processing 4 All (NLP4All). The intended purpose of NLP4All is to help teachers facilitate learning with and about NLP, by providing easy-to-use interfaces to NLP-methods, data, and analyses, making it possible for non- and novice-programmers to learn NLP concepts interactively. ©2021 Association for Computational Linguistics.

关键词： natural language processing systems

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Story Point Level Classification by Text Level graph Neural Network

Story Point Level Classification by Text Level Graph Neural ...

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natural language-based Software Engineering (NLBSE), IEEE/ACM International workshop on

作者： Hung Phan Ali Jannesari Iowa State University Ames Iowa USA

ISBN: (纸本)9781665462310

Estimating the software projects’ efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours and developers are required to complete the tasks. There are research works on automatic predicting the software efforts, including Term Frequency - Inverse Document Frequency (TFIDF) as the traditional approach for this problem. graph Neural Network is a new approach that has been applied in natural language processing for text classification. The advantages of graph Neural Network are based on the ability to learn information via graph data structure, which has more representations such as the relationships between words compared to approaches of vectorizing sequence of words. In this paper, we show the potential and possible challenges of graph Neural Network text classification in story point level estimation. By the experiments, we show that the GNN Text Level Classification can achieve as high accuracy as about 80% for story points level classification, which is comparable to the traditional approach. We also analyze the GNN approach and point out several current disadvantages that the GNN approach can improve for this problem or other problems in software engineering.

关键词： Conferences Text categorization Estimation Data structures graph neural networks Software natural language processing

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