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检索条件"丛书名=Lecture notes in artificial intelligence,"

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MFBE: Leveraging Multi-field Information of FAQs for Efficient Dense Retrieval 27th

MFBE: Leveraging Multi-field Information of FAQs for Efficie...

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Banerjee, Debopriyo Jain, Mausam Kulkarni, Ashish Rakuten India Enterprise Pvt Ltd Rakuten Inst Technol Bengaluru India

ISBN: (纸本)9783031333798;9783031333804

In the domain of question-answering in NLP, the retrieval of Frequently Asked Questions (FAQ) is an important sub-area which is well researched and has been worked upon for many languages. Here, in response to a user query, a retrieval system typically returns the relevant FAQs from a knowledge-base. The efficacy of such a system depends on its ability to establish semantic match between the query and the FAQs in real-time. The task becomes challenging due to the inherent lexical gap between queries and FAQs, lack of sufficient context in FAQ titles, scarcity of labeled data and high retrieval latency. In this work, we propose a bi-encoder-based query-FAQ matching model that leverages multiple combinations of FAQ fields (like, question, answer, and category) both during model training and inference. Our proposed Multi-Field Bi-Encoder (MFBE) model benefits from the additional context resulting from multiple FAQ fields and performs well even with minimal labeled data. We empirically support this claim through experiments on proprietary as well as open-source public datasets in both unsupervised and supervised settings. Our model achieves around 27% and 23% better top-1 accuracy for the FAQ retrieval task on internal and open datasets, respectively over the best performing baseline.

关键词： Information Retrieval FAQ Retrieval Question-Answering Multi-field BERT Bi-encoder

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Automatic Generation of Coherent Natural Language Texts 15th

Automatic Generation of Coherent Natural Language Texts

引用

15th International Conference on Flexible Query Answering Systems (FQAS)

作者： Marchenko, Oleksandr Isoieva, Mariam Taras Shevchenko Natl Univ Kyiv 60 Volodymyrska St UA-01033 Kiev Ukraine Int Res & Training Ctr Informat Technol & Syst Kiev Ukraine

ISBN: (纸本)9783031429347;9783031429354

Text generation models are widespread nowadays, there are more and more use cases for them. The majority of the most prominent and efficient, in terms of output quality, ones require vast computational resources for training and inference, which becomes an obstacle for their practical usage. However, even for big and powerful architectures, the texts they generate often lack coherence, logical links can be broken, which makes texts less readable and less useful. This paper presents a method for increasing coherence of text generated by neural network models and we emphasize the importance of languages' nature analysis prior to building new generation methods. We have analysed the existing decoding methods and have built the mechanism for maximizing coherence of the output sequences into some of them.

关键词： Natural Language Texts Generation Text Coherence Natural Language Texts Processing Decoding Methods in Generative Neural Networks Semantic Analysis

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Multi-granularity Feature Fusion for Transformer-Based Single Object Tracking

Multi-granularity Feature Fusion for Transformer-Based Singl...

引用

International Joint Conference on Rough Sets (IJCRS)

作者： Wang, Ziye Miao, Duoqian Tongji Univ Dept Comp Sci & Technol 4800 Caoan Highway Shanghai 201804 Peoples R China

ISBN: (纸本)9783031509582;9783031509599

The recently developed transformer has been largely explored in the research field of computer vision and especially improve the performance of single object tracking. However, the majority of current efforts concentrate on combining and enhancing convolutional neural network (CNN)-generated features and cannot fully excavating the potential of transformer. Motivated by this, we introduce multi-granularity theory into the pure transformer-based single object tracker and design a multi-granularity feature fusion module. With a view to fuse the feature of different granularity and enhance the feature representation, we design the double-branch transformer feature extractor and utilize cross-attention mechanism to fuse the feature. In our extensive experiments on multiple tracking benchmarks, including OTB2015, VOT2020, TrackingNet, GOT-10k, LaSOT, our proposed method named MGTT, the results could demonstrate that the proposed tracker achieves better performance than multiple state-of-the-art trackers.

关键词： Computer vision Single object tracking Multi granularity Rough set Transformer

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Class-Conditional Label Noise in Astroparticle Physics 1

引用

5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Bunse, Mirko Pfahler, Lukas TU Dortmund Univ Artificial Intelligence Grp D-44227 Dortmund Germany

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

Class-conditional label noise characterizes classification tasks in which the training set labels are randomly flipped versions of the actual ground-truth. The analysis of telescope data in astroparticle physics poses this problem with a novel condition: one of the class-wise label flip probabilities is known while the other is not. We address this condition with an objective function for optimizing the decision thresholds of existing classifiers. Our experiments on several imbalanced data sets demonstrate that accounting for the known label flip probability substantially improves the learning outcome over existing methods for learning under class-conditional label noise. In astroparticle physics, our proposal achieves an improvement in predictive performance and a considerable reduction in computational requirements. These achievements are a direct result of our proposal's ability to learn from real telescope data, instead of relying on simulated data as is common practice in the field.

关键词： Class-conditional label noise Imbalanced classification Astroparticle physics

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Towards Developing an Automated Chatbot for Predicting Legal Case Outcomes: A Deep Learning Approach 15th

Towards Developing an Automated Chatbot for Predicting Legal...

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15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Alam, Shafiq Pande, Rohit Ayub, Muhammad Sohaib Khan, Muhammad Asad Massey Univ Sch Management Auckland New Zealand Lahore Univ Management Sci Dept Comp Sci Lahore Pakistan Duco Consultancy Ltd Gurgaon India Hazara Univ Dept Telecommun Mansehra Pakistan

ISBN: (纸本)9789819958337;9789819958344

The accurate prediction of legal case outcomes is crucial for effective legal advocacy, which relies on a deep understanding of past cases. Our research aims to develop an automated chatbot for predicting the outcomes of employment-related legal cases using deep learning techniques. We compare and significantly improve on mining the New Zealand Employment Relations Authority (NZERA) dataset, using various deep learning models such as Latent Dirichlet Allocation (LDA) with different activation functions of Recurrent Neural Network (RNN) to determine their predictive performance. Our study's findings show that SoftSign-based RNN-LDA models have the highest accuracy and consistency in predicting outcomes.

关键词： Legal advocacy Predictive models Semantic analysis Deep learning

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Deep Learning-Based Tree Stem Segmentation for Robotic Eucalyptus Selective Thinning Operations 22nd

Deep Learning-Based Tree Stem Segmentation for Robotic Eucal...

引用

22nd EPIA Conference on artificial intelligence (EPIA)

作者： da Silva, Daniel Queiros Rodrigues, Tiago Ferreira Sousa, Armando Jorge dos Santos, Filipe Neves Filipe, Vitor Univ Tras Os Montes & Alto Douro UTAD Vila Real Portugal INESC Technol & Sci INESC TEC Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal

ISBN: (纸本)9783031490101;9783031490118

Selective thinning is a crucial operation to reduce forest ignitable material, to control the eucalyptus species and maximise its profitability. The selection and removal of less vigorous stems allows the remaining stems to grow healthier and without competition for water, sunlight and nutrients. This operation is traditionally performed by a human operator and is time-intensive. This work simplifies selective thinning by removing the stem selection part from the human operator's side using a computer vision algorithm. For this, two distinct datasets of eucalyptus stems (with and without foliage) were built and manually annotated, and three Deep Learning object detectors (YOLOv5, YOLOv7 and YOLOv8) were tested on real context images to perform instance segmentation. YOLOv8 was the best at this task, achieving an Average Precision of 74% and 66% on non-leafy and leafy test datasets, respectively. A computer vision algorithm for automatic stem selection was developed based on the YOLOv8 segmentation output. The algorithm managed to get a Precision above 97% and a 81% Recall. The findings of this work can have a positive impact in future developments for automatising selective thinning in forested contexts.

关键词： Computer vision Deep learning Forestry Instance segmentation Selective thinning

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Two-Sorted Modal Logic for Formal and Rough Concepts

Two-Sorted Modal Logic for Formal and Rough Concepts

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International Joint Conference on Rough Sets (IJCRS)

作者： Howlader, Prosenjit Liau, Churn-Jung Acad Sinica Inst Informat Sci Taipei 115 Taiwan

ISBN: (纸本)9783031509582;9783031509599

In this paper, we propose two-sorted modal logics for the representation and reasoning of concepts arising from rough set theory (RST) and formal concept analysis (FCA). These logics are interpreted in two-sorted bidirectional frames, which are essentially formal contexts with converse relations. The logic KB contains ordinary necessity and possibility modalities and can represent rough set-based concepts. On the other hand, the logic KF has window modality that can represent formal concepts. We study the relationship between KB and KF by proving a correspondence theorem. It is then shown that, using the formulae with modal operators in KB and KF, we can capture formal concepts based on RST and FCA and their lattice structures.

关键词： Modal logic Formal concept analysis Rough set theory

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Generating Robust Counterfactual Explanations

Generating Robust Counterfactual Explanations

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Guyomard, Victor Fessant, Francoise Guyet, Thomas Bouadi, Tassadit Termier, Alexandre Orange Innovat Lannion France Univ Rennes CNRS INRIA IRISA Rennes France INRIA AIstroSight Paris France

ISBN: (纸本)9783031434174;9783031434181

Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction. The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, etc. In this paper, we are interested in the notion of robustness of a counterfactual. More precisely, we focus on robustness to counterfactual input changes. This form of robustness is particularly challenging as it involves a trade-off between the robustness of the counterfactual and the proximity with the example to explain. We propose a new framework, CROCO, that generates robust counterfactuals while managing effectively this trade-off, and guarantees the user a minimal robustness. An empirical evaluation on tabular datasets confirms the relevance and effectiveness of our approach.

关键词： Counterfactual explanation Robustness Algorithmic recourse

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An Ethical Perspective on Intelligent Transport Systems 22nd

An Ethical Perspective on Intelligent Transport Systems

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： da Costa, Antonio Ribeiro Kokkinogenis, Zafeiris Rossetti, Rosaldo J. F. Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031490101;9783031490118

Intelligent Transport Systems (ITS) is a fast evolving domain with an increasingly important role in shaping the future of transport and a significant impact on a wide range of issues, many of which have ethical implications. On the other hand, Ethics is essential to ensure that ITS are safe, fair, accountable, trustworthy, and respectful of privacy. This study reflects on the ethical concerns around transport system and its impact on economic, social and environmental dimensions, from the spirit of the foundational concepts of Ethics to the specific issues raised by intelligent transport, including those enhanced by artificial intelligence (AI) and Machine Learning (ML) systems. The primordial ethical concerns of transport have, in some extent, been mitigated with the introduction of the ITS paradigm, but others have arisen as a result of emerging technologies. Ethics is therefore critical in intelligent transport because of its potential to significantly impact individuals, communities, and society as a whole, and is an important tool to design more sustainable, equitable, and fair transport systems.

关键词： Transport systems Ethics Intelligent transport systems

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Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation 18th

Efficient Computation of Shap Explanation Scores for Neural ...

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： Bertossi, Leopoldo Leon, Jorge E. SKEMA Business Sch Montreal PQ Canada Univ Adolfo Ibanez UAI Santiago Chile

ISBN: (纸本)9783031436185;9783031436192

The use of Shap scores has become widespread in Explainable AI. However, their computation is in general intractable, in particular when done with a black-box classifier, such as neural network. Recent research has unveiled classes of open-box Boolean Circuit classifiers for which Shap can be computed efficiently. We show how to transform binary neural networks into those circuits for efficient Shap computation. We use logic-based knowledge compilation techniques. The performance gain is huge, as we show in the light of our experiments.

关键词： Computational efficiency

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