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SegRewardgraph: unsupervised teaching video story segmentation method based on subtitle length-rewarding strategy and semantic relatedness graphs

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期83卷 52509页

作者： Liu, Zhi Chen, Hao Kong, Xi Wen, Chaodong Chen, Jia Liu, Sannyuya Yang, Zongkai Cent China Normal Univ Natl Engn Res Ctr Elearning Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Natl Engn Lab Educ Big Data Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Sch Educ Informat Technol Wuhan 430079 Hubei Peoples R China

Story segmentation plays an important role in helping students quickly locate key knowledge points within massive and fragmented teaching video resources. It also serves as the basis for accurate searching of valuable teaching video clips. While keyframe-based story segmentation has achieved remarkable results in news videos, achieving accurate story segmentation in teaching videos is challenging. Therefore, we propose an unsupervised teaching video story segmentation method called SegRewardgraph, which utilizes a subtitle length-rewarding strategy and semantic relatedness graphs. SegRewardgraph employs subtitle semantic similarity to divide video stories, ensuring semantic integrity and improving the accuracy of story segmentation. Specifically, SegRewardgraph first uses the Bidirectional Encoder Representation from Transformers (BERT) model combined with first-last-avg pooling to encode semantic vectors for sentences within video stories. Then, it computes similarities between all sentence vectors and utilizes the associations to build semantic relatedness graphs. A subtitle length-rewarding strategy is formulated to evaluate the segmentation effect. Additionally, boundary detection and boundary merging algorithms are designed based on the subtitle length-rewarding strategy to generate effective segmentation suggestions. Finally, the selected boundaries are mapped to keyframes, enabling semantic content-based segmentation of teaching videos. This study verifies the effectiveness of the proposed method using massive open online courses (MOOC) teaching video datasets. Experimental results demonstrate that the model proposed in this paper outperforms existing methods and achieves state-of-the-art results for teaching video segmentation tasks.

关键词： Story segmentation Teaching video segmentation Subtitle length-rewarding strategy Semantic relatedness graph natural language processing

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Empowering Hardware Security with LLM: The Development of a Vulnerable Hardware Database

Empowering Hardware Security with LLM: The Development of a ...

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International Symposium on Hardware Oriented Security and Trust (HOST)

作者： Saha, Dipayan Yahyaei, Katayoon Saha, Sujan Kumar Tehranipoor, Mark Farahmandi, Farimah Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9798350373950;9798350373943

The scarcity of comprehensive databases and benchmarks in hardware design specifically tailored for security tasks is a significant challenge in the community. Such databases are crucial for developing machine learning-based methods and benchmarking, providing a foundation for evaluating and improving hardware security solutions. However, manually creating these extensive datasets is impractical due to the significant time and effort required. Given the proficiency of large language models (LLM) in natural language processing, coding, and advanced reasoning tasks, using LLM as an artificial intelligence (AI) agent presents a viable option to efficiently create such extensive datasets. In this light, this paper introduces Vul-FSM, a database of 10,000 vulnerable finite state machine (FSM) designs incorporating 16 distinct security weaknesses and vulnerabilities generated using the proposed SecRT-LLM framework. The framework combines the in-context learning capability of LLM, the guidance of developed prompting strategies, and the scrutiny of fidelity-check to not only insert but also detect hardware vulnerabilities and weaknesses. To demonstrate the efficacy of SecRT-LLM, we present an exhaustive analysis, highlighting the proficiency of GPT models in vulnerability insertion, detection, and mitigation. Our proposed SecRT-LLM framework, using gpt-3.5-turbo, demonstrates strong effectiveness, achieving macro-average pass rates of 81.98% and 80.30% on the first attempt and 97.37% and 99.07% within five attempts for vulnerability insertion and detection, respectively.

关键词： Large language Model ChatGPT RTL design Hardware Security Vulnerability Analysis Common Weakness Enumeration (CWE)

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iLPCtd: Interpretable Link Prediction on Continuous Time Durations with Extracted Logical Rules from Temporal Knowledge graphs 7

iLPCtd: Interpretable Link Prediction on Continuous Time Dur...

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7th International Conference on Machine Learning and natural language processing, MLNLP 2024

作者： Yang, Shijie Du, Shaochong Lu, Yongzhan Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350354973

Temporal Knowledge graphs (TKGs) augment traditional knowledge graphs by integrating temporal attributes, transforming them into a more nuanced and dynamic data structure capable of modeling real-world events. Representation learning techniques, particularly embedding-based methods, have been widely adopted for reasoning over TKGs. However, these methods often suffer from a black-box nature, which limits their ability to provide transparent reasoning chains and logically consistent link prediction outcomes. Moreover, many of these approaches overlook the duration of events, despite the fact that most real-world events span over periods of time rather than occurring instantaneously. This paper proposes a novel model, Interpretable Link Prediction on Continuous Time Durations (iLPCtd), which addresses both the interpretability of link predictions and the consideration of continuous temporal dynamics. By leveraging a random walk approach, iLPCtd extracts first-order temporal logical rules from given TKGs and applies them for subsequent link predictions. Importantly, the model incorporates the continuous nature of event timestamps to more accurately reflect real-world scenarios. To evaluate the model's performance, we constructed a new dataset, Satellite Observation to Earth (SOE), based on satellite meteorological observation workflows. Experimental results demonstrate that iLPCtd outperforms existing methods on this dataset. Additionally, experiments conducted on public datasets also show promising results, indicating that iLPCtd is not only capable of generating interpretable logical reasoning chains but also excels in handling complex temporal link prediction tasks, offering both theoretical and practical improvements in TKG reasoning. © 2024 IEEE.

关键词： Knowledge graph

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TASTE: Teaching Large language Models to Translate through Self-Reflection 62

TASTE: Teaching Large Language Models to Translate through S...

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62nd Annual Meeting of the Association-for-Computational-Linguistics (ACL) / Student Research workshop (SRW)

作者： Wang, Yutong Zeng, Jiali Liu, Xuebo Meng, Fandong Zhou, Jie Zhang, Min Harbin Inst Technol Inst Comp & Intelligence Shenzhen Peoples R China Tencent Inc Pattern Recognit Ctr WeChat AI Shenzhen Peoples R China

ISBN: (纸本)9798891760943

Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the proficiency of LLMs in the downstream task of machine translation. However, the existing approaches fail to yield satisfactory translation outputs that match the quality of supervised neural machine translation (NMT) systems. One plausible explanation for this discrepancy is that the straightforward prompts employed in these methodologies are unable to fully exploit the acquired instruction-following capabilities. To this end, we propose the TASTE framework, which stands for translating through self-reflection. The self-reflection process includes two stages of inference. In the first stage, LLMs are instructed to generate preliminary translations and conduct self-assessments on these translations simultaneously. In the second stage, LLMs are tasked to refine these preliminary translations according to the evaluation results. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods. Our work presents a promising approach to unleash the potential of LLMs and enhance their capabilities in MT. The codes and datasets are open-sourced at https://***/YutongWang1216/ReflectionLLMMT.

关键词： Computational linguistics

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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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Knowledge graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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Data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain Knowledge embedding Knowledge attention Mutual attention

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Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules

Logical Neural Networks for Knowledge Base Completion with E...

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2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Sen, Prithviraj de Carvalho, Breno W.S.R. Abdelaziz, Ibrahim Kapanipathi, Pavan Roukos, Salim Gray, Alexander Amazon IBM Research

Knowledge base completion (KBC) has benefitted greatly by learning explainable rules in an human-interpretable dialect such as first-order logic. Rule-based KBC has so far, mainly focussed on learning one of two types of rules: conjunction-of-disjunctions and disjunction-of-conjunctions. We qualitatively show, via examples, that one of these has an advantage over the other when it comes to achieving high quality KBC. To the best of our knowledge, we are the first to propose learning both kinds of rules within a common framework. To this end, we propose to utilize logical neural networks (LNN) (Riegel et al., 2020), a powerful neuro-symbolic AI framework that can express both kinds of rules and learn these end-to-end using gradient-based optimization. Our in-depth experiments show that our LNN-based approach to learning rules for KBC leads to roughly 10% relative improvements, if not more, over SotA rule-based KBC methods. Moreover, by showing how to combine our proposed methods with knowledge graph embeddings we further achieve additional 7.5% relative improvement. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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What Changed? Investigating Debiasing methods using Causal Mediation Analysis 4

What Changed? Investigating Debiasing Methods using Causal M...

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4th workshop on Gender Bias in natural language processing, GeBNLP 2022

作者： Jeoung, Sullam Diesner, Jana University of Illinois-Urbana Champaign United States

ISBN: (纸本)9781955917681

Previous work has examined how debiasing language models affect downstream tasks, specifically, how debiasing techniques influence task performance and whether debiased models also make impartial predictions in downstream tasks or not. However, what we don't understand well yet is why debiasing methods have varying impacts on downstream tasks and how debiasing techniques affect internal components of language models, i.e., neurons, layers, and attentions. In this paper, we decompose the internal mechanisms of debiasing language models with respect to gender by applying causal mediation analysis to understand the influence of debiasing methods on toxicity detection as a downstream task. Our findings suggest a need to test the effectiveness of debiasing methods with different bias metrics, and to focus on changes in the behavior of certain components of the models, e.g.,first two layers of language models, and attention heads. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Multilingual language Model with Massive Multilingual Knowledge Triples

Enhancing Multilingual Language Model with Massive Multiling...

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2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Liu, Linlin Li, Xin He, Ruidan Bing, Lidong Joty, Shafiq Si, Luo DAMO Academy Alibaba Group China Nanyang Technological University Singapore Salesforce Research

Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utilization of multilingual knowledge graph (KG) data for language model (LM) pretraining. They often train LMs with KGs in indirect ways, relying on extra entity/relation embeddings to facilitate knowledge injection. In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples. We first generate a large amount of multilingual synthetic sentences using the Wikidata KG triples. Then based on the intra- and inter-sentence structures of the generated data, we design pretraining tasks to enable the LMs to not only memorize the factual knowledge but also learn useful logical patterns. Our pretrained KMLMs demonstrate significant performance improvements on a wide range of knowledge-intensive crosslingual tasks, including named entity recognition (NER), factual knowledge retrieval, relation classification, and a newly designed logical reasoning task. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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Text-Aware graph Embeddings for Donation Behavior Prediction 16

Text-Aware Graph Embeddings for Donation Behavior Prediction

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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： Dong, MeiXing Xu, Xueming Mihalcea, Rada University of Michigan United States

Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior. © 2022 proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Forecasting

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