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检索条件"任意字段=15th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2021"

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textgraphs 2021 - graph-based methods for natural language processing, Proceedings of the 15th workshop - in conjunction with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2021

TextGraphs 2021 - Graph-Based Methods for Natural Language P...

引用

15th workshop on graph-based methods for natural language processing, textgraphs 2021

ISBN: (纸本)9781954085381

the proceedings contain 20 papers. the topics discussed include: bootstrapping large-scale fine-grained contextual advertising classifier from Wikipedia;modeling graph structure via relative position for text generation from knowledge graphs;entity prediction in knowledge graphs with joint embeddings;hierarchical graph convolutional networks for jointly resolving cross-document coreference of entity and event mentions;learning clause representation from dependency-anchor graph for connective prediction;selective attention based graph convolutional networks for aspect-level sentiment classification;keyword extraction using unsupervised learning on the document’s adjacency matrix;improving human text simplification with sentence fusion;and on geodesic distances and contextual embedding compression for text classification.

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textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings 15

Textgraphs-15 Shared Task System Description : Multi-Hop Inf...

引用

15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Kalyan, Vivek Witteveen, Sam Andrews, Martin Singapore Singapore Red Dragon AI Singapore

ISBN: (纸本)9781954085381

Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. this year, to refocus the textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single 'correct path'), the WorldTree dataset was augmented with expert ratings of 'relevance' of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval;language models trained to predict the relevance scores;and ensembling of a number of the resulting rankings. Our code implementation is made available at https: //***/mdda/worldtree_ corpus/tree/textgraphs_2021. © 2021 Association for Computational Linguistics.

关键词： Reforestation

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MG-BERT: Multi-graph Augmented BERT for Masked language Modeling 15

MG-BERT: Multi-Graph Augmented BERT for Masked Language Mode...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Ghader, Parishad Behnam Zakerinia, Hossein Baghshah, Mahdieh Soleymani Sharif University of Technology Tehran Iran

ISBN: (纸本)9781954085381

Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in natural language processing (NLP) tasks. However, there are still some shortcomings in the Masked language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. then, we propose Multi-graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taking advantage of a static multi-graph containing global word co-occurrences in the text corpus beside global real-world facts about words in knowledge graphs. the proposed model also employs a dynamic sentence graph to capture local context effectively. Experimental results demonstrate that our model can considerably enhance the performance in the MLM task. © 2021 Association for Computational Linguistics.

关键词： Knowledge graph

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COLING 2022 - Proceedings of textgraphs-16: graph-based methods for natural language processing, NLP 2022 - 29th International Conference on Computational Linguistics

COLING 2022 - Proceedings of TextGraphs-16: Graph-based Meth...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

the proceedings contain 15 papers. the topics discussed include: multilevel hypernode graphs for effective and efficient entity linking;cross-modal contextualized hidden state projection method for expanding of taxonomic graphs;sharing parameter by conjugation for knowledge graph embeddings in complex space;a clique-based graphical approach to detect interpretable adjectival senses in Hungarian;the effectiveness of masked language modeling and adapters for factual knowledge injection;text-aware graph embeddings for donation behavior prediction;word sense disambiguation of French lexicographical examples using lexical networks;temporal graph analysis of misinformation spreaders in social media;IJS at textgraphs-16 natural language premise selection task: will contextual information improve natural language premise selection?;and keyword-based natural language premise selection for an automatic mathematical statement proving.

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Improving Human Text Simplification with Sentence Fusion 15

Improving Human Text Simplification with Sentence Fusion

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Schwarzer, Max Tanprasert, Teerapaun Kauchak, David Mila University of Montreal Canada Computer Science Department Pomona College United States

ISBN: (纸本)9781954085381

the quality of fully automated text simplification systems is not good enough for use in real-world settings;instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of human simplifications by leveraging crowdsourcing. We introduce a graph-based sentence fusion approach to augment human simplifications and a reranking approach to both select high quality simplifications and to allow for targeting simplifications with varying levels of simplicity. Using the Newsela dataset (Xu et al., 2015) we show consistent improvements over experts at varying simplification levels and find that the additional sentence fusion simplifications allow for simpler output than the human simplifications alone. © 2021 Association for Computational Linguistics.

关键词： graphic methods

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DeepBlueAI at textgraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem 15

DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Ho...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： Pan, Chunguang Song, Bingyan Luo, Zhipeng Co. Ltd China

ISBN: (纸本)9781954085381

this paper describes the winning system for textgraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. the official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is https://***/DeepBlueAI/textgraphs-15. © 2021 Association for Computational Linguistics.

关键词： Knowledge based systems

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textgraphs 2022 Shared Task on natural language Premise Selection 16

TextGraphs 2022 Shared Task on Natural Language Premise Sele...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Valentino, Marco Ferreira, Deborah thayaparan, Mokanarangan Freitas, André Ustalov, Dmitry Idiap Research Institute Switzerland University of Manchester United Kingdom Toloka Serbia

the Shared Task on natural language Premise Selection (NLPS) asks participants to retrieve the set of premises that are most likely to be useful for proving a given mathematical statement from a supporting knowledge base. While previous editions of the textgraphs shared tasks series targeted multi-hop inference for explanation regeneration in the context of science questions (thayaparan et al., 2021;Jansen and Ustalov, 2020, 2019), NLPS aims to assess the ability of state-of-the-art approaches to operate on a mixture of natural and mathematical language and model complex multi-hop reasoning dependencies between statements. To this end, this edition of the shared task makes use of a large set of approximately 21k mathematical statements extracted from the PS-ProofWiki dataset (Ferreira and Freitas, 2020a). In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of the results, evaluating various aspects involved in mathematical language processing and multi-hop inference. the best-performing system achieved a MAP of 15.39, improving the performance of a TF-IDF baseline by approximately 3.0 MAP.1 © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Large dataset

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Sharing Parameter by Conjugation for Knowledge graph Embeddings in Complex Space 16

Sharing Parameter by Conjugation for Knowledge Graph Embeddi...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Feng, Xincan Qu, Zhi Cheng, Yuchang Watanabe, Taro Yugami, Nobuhiro Natural Language Processing Laboratory Nara Institute of Science and Technology Japan Multilingual Knowledge Computing Laboratory Fujitsu Ltd Japan

A Knowledge graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse natural language processing (NLP) tasks where knowledge is required. the need to scale up and complete KG automatically yields Knowledge graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models 5*E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Directed graphs

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textgraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration 15

TextGraphs 2021 Shared Task on Multi-Hop Inference for Expla...

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15th workshop on graph-based methods for natural language processing, textgraphs 2021

作者： thayaparan, Mokanarangan Valentino, Marco Jansen, Peter Ustalov, Dmitry Department of Computer Science University of Manchester United Kingdom School of Information University of Arizona United States Crowdsourcing Research Group Yandex Russia

ISBN: (纸本)9781954085381

the Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to compose large multi-hop explanations to questions by assembling large chains of facts from a supporting knowledge base. While previous editions of this shared task aimed to evaluate explanatory completeness - finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations. To this end, this edition of the shared task makes use of a large set of approximately 250k manual explanatory relevancy ratings that augment the 2020 shared task data. In this summary paper, we describe the details of the explanation regeneration task, the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of participating systems, evaluating various aspects involved in the multi-hop inference process. the best performing system achieved an NDCG of 0.82 on this challenging task, substantially increasing performance over baseline methods by 32%, while also leaving significant room for future improvement. © 2021 Association for Computational Linguistics.

关键词： Knowledge based systems

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Resolving duplicates in Large Multiple-Choice Questions Repositories 20

Resolving duplicates in Large Multiple-Choice Questions Repo...

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20th Conference on Information and Research Science Connecting to Digital and Library Science, IRCDL 2024

作者： Albano, Valentina Firmani, Donatella Laura, Luigi Mathew, Jerin George Paoletti, Anna Lucia Torrente, Irene Dip. Funzione Pubblica Corso Vittorio Emanuele II 116 Rome00186 Italy Sapienza University Piazzale Aldo Moro 5 Rome00185 Italy Uninettuno University Corso Vittorio Emanuele II 39 Rome00186 Italy Formez Viale Marx 15 Rome00137 Italy

Multiple-choice questions (MCQs) are commonly used in educational assessments and professional certification examinations. However, managing vast collections of MCQs presents numerous challenges, including maintaining their quality and relevance. A notable issue in such repositories is the occurrence of conceptually identical questions presented in varied forms. these duplicates, while different in wording, fail to enhance the value of the repository. In this extended abstract, we present our approach for identifying and handling potential duplicate questions in large MCQ databases. Our proposed method involves three primary stages: initial pre-processing of MCQs, calculation of similarity based on natural language processing (NLP) techniques, and a graph-based method for exploring these similarities. © 2021 Copyright for this paper by its authors.

关键词： graphic methods

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