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Selective Temporal Knowledge Graph Reasoning 30

Selective Temporal Knowledge Graph Reasoning

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Hou, Zhongni Jin, Xiaolong Li, Zixuan Bai, Long Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inevitably bring risks in real-world applications. Thus, in this paper, we propose an abstention mechanism for TKG reasoning, which helps the existing models make selective, instead of indiscriminate, predictions. Specifically, we develop a confidence estimator, called Confidence Estimator with History (CEHis), to enable the existing TKG reasoning models to first estimate their confidence in making predictions, and then abstain from those with low confidence. To do so, CEHis takes two kinds of information into consideration, namely, the certainty of the current prediction and the accuracy of historical predictions. Experiments with representative TKG reasoning models on two benchmark datasets demonstrate the effectiveness of the proposed CEHis. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

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Nested Event Extraction upon Pivot Element Recognition 30

Nested Event Extraction upon Pivot Element Recognition

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Weicheng Li, Zixuan Jin, Xiaolong Bai, Long Su, Miao Liu, Yantao Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner-nest and outer-nest events and further recognizes the PEs via classifying the relation type between trigger pairs. The model uses prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations to improve NEE performance. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of the PerNee model are available at https://***/waysonren/PerNee. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Character recognition

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Pathfinding with Dynamic Obstacles

Pathfinding with Dynamic Obstacles

引用

Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Patil, Anshul Kamboj, Shreshtha Bajaj, Anu Abraham, Ajith Thapar Institute of Engineering and Technology Patiala India Faculty of Computing and Data Sciences Flame University Pune India

ISBN: (纸本)9783031397769

Pathfinding algorithms are often at the center of any AI movement system. It is required to provide agents mobility in developing a realistic artificial intelligence (AI) for real-world scenarios. Its main goal is to find the optimized path from source to destination by avoiding obstacles. Finding a path through a maze is a major issue as it can be complex due to maze size, number of obstacles and have multiple routes that may lead to dead end. This paper proposed a search algorithm that finds the shortest path with dynamically created obstacles between the source and destination. It uses the A star algorithm and Manhattan distance as heuristic measure. The experiment was performed on grids of 10 × 10 to 100 × 100 size with step size of 10 × 10 on different obstacles ranges. The algorithm’s overall performance is respectable, and it can determine the quickest route between any two points. The proposed algorithm will always return a path for less than 30% obstacles and in cases where feasible paths can be generated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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FLEXNet: Learning Deep Features Using Autoencoders for Floor Plan Image Retrieval

FLEXNet: Learning Deep Features Using Autoencoders for Floor...

引用

2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Khade, Rasika Kalsekar, Atharva Jariwala, Krupa Chattopadhyay, Chiranjoy Svnit Department of Cse Surat India Flame School of Computing and Data Sciences Flame University Pune India

ISBN: (纸本)9798350337785

Content-based retrieval of scanned document images is gaining research. A system that automatically analyses and retrieves similar floor plan images depending on user needs might help architects create new ideas and provide client recommendations. Our model, FLEXNet (Floor pLan EXplorer Network), extracts deep features from images using an Autoencoder-based approach and facilitates exploration of a database of floor plan images to retrieve similar images using a K-nearest neighbour classifier. We evaluated FLEXNet on images perturbed with rotation, scaling, and noise in a publicly available dataset. FLEXNet outperforms state-of-the-art methods with an 85% mean average accuracy. © 2023 IEEE.

关键词： Content based retrieval

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Software Test Suite Minimization Using Hybrid Metaheuristics

Software Test Suite Minimization Using Hybrid Metaheuristics

引用

Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Bajaj, Anu Abraham, Ajith Sambyal, Nitigya Thapar Institute of Engineering and Technology Patiala India Faculty of Computing and Data Sciences Flame University Pune India

ISBN: (纸本)9783031397738

Software testing is a crucial requirement for software quality. Continuous integration and continuous development lead to the inevitable growth of test cases, resulting in potential redundancies. It is a laborious and time consuming task to perform exhaustive re-testing. Test case minimization is a regression testing technique that filters and removes redundant test cases to reduce time and cost. It is like the minimal set cover problem, which satisfies some constraints with limited resources. Optimization algorithms are possible candidates to solve such NP-hard problems. Therefore, this paper presents the application of the hybrid of Bat algorithm and differential evolution (BADE) which takes into account the interference from prey and companions for regression test suite reduction. Two improvements were introduced to this hybrid algorithm 1) by changing the intensity of the interference (IBADEc) and 2) use of elite bats to enhance the exploitation at early stages (IBADEe). The proposed algorithms have been compared with the existing nature-inspired algorithms. The experimental results show that the proposed hybrid algorithm IBADEe outperformed other algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software testing

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Class-Incremental Few-Shot Event Detection 30

Class-Incremental Few-Shot Event Detection

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classes constantly. These new classes usually have only a few labeled instances as it is time-consuming and labor-intensive to annotate a large number of unlabeled instances. Therefore, this paper proposes a new task, called class-incremental few-shot event detection. Nevertheless, this task faces two problems, i.e., old knowledge forgetting and new class overfitting. To solve these problems, this paper further presents a novel knowledge distillation and prompt learning based method, called Prompt-KD. Specifically, to handle the forgetting problem about old knowledge, Prompt-KD develops an attention based multi-teacher knowledge distillation framework, where the ancestor teacher model pre-trained on base classes is reused in all learning sessions, and the father teacher model derives the current student model via adaptation. On the other hand, in order to cope with the few-shot learning scenario and alleviate the corresponding new class overfitting problem, Prompt-KD is also equipped with a prompt learning mechanism. Extensive experiments on two benchmark datasets, i.e., FewEvent and MAVEN, demonstrate the superior performance of Prompt-KD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions 38

Cracking the Code of Juxtaposition: Can AI Models Understand...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Zhe Liang, Tuo Li, Jing Lu, Yiren Zhou, Yunlai Qiao, Yiran Ma, Jing Yin, Yu Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer and Data Sciences Case Western Reserve University United States

Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction. We introduce the YESBUT benchmark, which comprises tasks of varying difficulty aimed at assessing AI's capabilities in recognizing and interpreting these comics, ranging from literal content comprehension to deep narrative reasoning. Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics. Our results show that even state-of-the-art models still lag behind human performance on this task. Our findings offer insights into the current limitations and potential improvements for AI in understanding human creative expressions. © 2024 Neural information processing systems foundation. All rights reserved.

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A Deep Dive into Robust Optimization Over Time: Problems, Algorithms, and Beyond

A Deep Dive into Robust Optimization Over Time: Problems, Al...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Yazdani, Danial Yao, Xin Data Science Institute University of Technology Sydney Sydney Australia Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong

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Disentangling Linear Quadratic Control with Untrusted ML Predictions 38

Disentangling Linear Quadratic Control with Untrusted ML Pre...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Tongxin Liu, Hao Yue, Yisong School of Data Science The Chinese University of Hong Kong Shenzhen China Computing + Mathematical Sciences California Institute of Technology Pasadena United States

Uncertain perturbations in dynamical systems often arise from diverse resources, represented by latent components. The predictions for these components, typically generated by "black-box" machine learning tools, are prone to inaccuracies. To tackle this challenge, we introduce DISC, a novel policy that learns a confidence parameter online to harness the potential of accurate predictions while also mitigating the impact of erroneous forecasts. When predictions are precise, DISC leverages this information to achieve near-optimal performance. Conversely, in the case of significant prediction errors, it still has a worst-case competitive ratio guarantee. We provide competitive ratio bounds for DISC under both linear mixing of latent variables as well as a broader class of mixing functions. Our results highlight a first-of-its-kind "best-of-both-worlds" integration of machine-learned predictions, thus lead to a near-optimal consistency and robustness tradeoff, which provably improves what can be obtained without learning the confidence parameter. We validate the applicability of DISC across a spectrum of practical scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

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