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Boosting legal case retrieval by query content selection with large language models

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arXiv 2023年

作者： Zhou, Youchao Huang, Heyan Wu, Zhijing School of Computer Science and Technology Beijing Institute of Technology Southeast Academy of Information Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long and the definition of relevance is closely related to legal-specific elements. Therefore, legal case queries may suffer from noise and sparsity of salient content, which hinders retrieval models from perceiving correct information in a query. While previous studies have paid attention to improving retrieval models and understanding relevance judgments, we focus on enhancing legal case retrieval by utilizing the salient content in legal case queries. We first annotate the salient content in queries manually and investigate how sparse and dense retrieval models attend to those content. Then we experiment with various query content selection methods utilizing large language models (LLMs) to extract or summarize salient content and incorporate it into the retrieval models. Experimental results show that reformulating long queries using LLMs improves the performance of both sparse and dense models in legal case retrieval. © 2023, CC BY-NC-SA.

关键词： Computational linguistics

Distant supervision for relation extraction with linear attenuation simulation and non-IID relevance embedding

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arXiv 2018年

作者： Yuan, Changsen Huang, Heyan Feng, Chong Liu, Xiao Wei, Xiaochi Department of Computer Science Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Baidu Inc. China

Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem. However, existing distant supervision methods suffer from selecting important words in the sentence and extracting valid sentences in the bag. Towards this end, we propose a novel approach to address these problems in this paper. Firstly, we propose a linear attenuation simulation to reflect the importance of words in the sentence with respect to the distances between entities and words. Secondly, we propose a non-independent and identically distributed (non-IID) relevance embedding to capture the relevance of sentences in the bag. Our method can not only capture complex information of words about hidden relations, but also express the mutual information of instances in the bag. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Unifying Cross-lingual Summarization and Machine Translation with Compression Rate

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arXiv 2021年

作者： Bai, Yu Huang, Heyan Fan, Kai Gao, Yang Zhu, Yiming Zhan, Jiaao Chi, Zewen Chen, Boxing School of Computer Science Beijing Institute of Technology Beijing China Southeast Academy of Information Technology Fujian China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Cross-Lingual Summarization (CLS) is a task that extracts important information from a source document and summarizes it into a summary in another language. It is a challenging task that requires a system to understand, summarize, and translate at the same time, making it highly related to Monolingual Summarization (MS) and Machine Translation (MT). In practice, the training resources for Machine Translation are far more than that for cross-lingual and monolingual summarization. Thus incorporating the Machine Translation corpus into CLS would be beneficial for its performance. However, the present work only leverages a simple multi-task framework to bring Machine Translation in, lacking deeper exploration. In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit Cross-Lingual Summarization by large-scale Machine Translation corpus. Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%. Hence they can be trained as a unified task, sharing knowledge more effectively. However, a huge gap exists between the MT task and the CLS task, where samples with compression rates between 30% and 90% are extremely rare. Hence, to bridge these two tasks smoothly, we propose an effective data augmentation method to produce document-summary pairs with different compression rates. The proposed method not only improves the performance of the CLS task, but also provides controllability to generate summaries in desired lengths. Experiments demonstrate that our method outperforms various strong baselines in three cross-lingual summarization datasets. We released our code and data at https://***/ybai-nlp/CLS_CR. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics

SDN controllers: Benchmarking & performance evaluation

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arXiv 2019年

作者： Zhu, Liehuang Karim, Md Monjurul Sharif, Kashif Li, Fan Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center for Massive Language Information Processing & Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. More specifically, we categorize and classify 34 controllers based on their capabilities, and present a qualitative comparison of their properties. We also discuss in-depth capabilities of benchmarking tools used for SDN controllers, along with best practices for quantitative controller evaluation. This work uses three benchmarking tools to compare nine controllers against multiple criteria. Finally, we discuss detailed research findings on the performance, benchmarking criteria, and evaluation testbeds for SDN controllers. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Sharing Pre-trained BERT Decoder for a Hybrid Summarization

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Sharing Pre-trained BERT Decoder for a Hybrid Summarization

第十八届中国计算语言学大会暨中国中文信息学会2019学术年会

作者： Ran Wei Heyan Huang Yang Gao School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing 100081China

ISBN: (纸本)9783030323806

Sentence selection and summary generation are two main steps to generate informative and readable ***,most previous works treat them as two separated *** this paper,we propose a novel extractive-and-abstractive hybrid framework for single document summarization task by jointly learning to select sentence and rewrite *** first selects sentences by an extractive decoder and then generate summary according to each selected sentence by an abstractive ***,we apply the BERT pre-trained model as document encoder,sharing the context representations to both *** on the CNN/DailyMail dataset show that the proposed framework outperforms both state-of-the-art extractive and abstractive models.

关键词： Text Summarization Extractive and Abstractive Pretrained Based

Fault-Tolerant Topology for Energy-Harvesting Heterogeneous Wireless Sensor Networks

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Fault-Tolerant Topology for Energy-Harvesting Heterogeneous ...

IEEE International Conference on Communications

作者： Zhiyuan Yin Fan Li Meng Shen Yu Wang Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781467364300

Recent advances in ambient energy-harvesting wireless sensor networks (WSNs) technologies have made it possible to power the network by energy generated from the environment and thereby increase its lifetime. Various energy sources including light, vibration and heat can be harvested by sensor nodes. However, time-varying energy harvesting also bring new design challenging for WSNs. In this paper, we study a fault-tolerant topology design problem for an energy-harvesting heterogeneous WSN, where multiple supernodes with rich resources are used to improve the performance. We first model the network as a directed and weighted space-time graph in which both spacial and temporal information are preserved. We then define the fault-tolerant topology problem which aims to build a sparser time-varying structure from the original space-time graph while maintaining k-connectivity for the fault-tolerant purpose. Six different algorithms are proposed to solve the problem. Simulation results demonstrate that our proposed methods can save up to around 80% costs.

关键词： Topology Fault tolerance Fault tolerant systems Wireless sensor networks Network topology Ad hoc networks Algorithm design and analysis

ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension

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arXiv 2022年

作者： Zhang, Xiao Huang, Heyan Chi, Zewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning;(2) span extraction if required by the above decision;(3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available1 Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

SciMRC: Multi-perspective Scientific Machine Reading Comprehension

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arXiv 2023年

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scientific machine reading comprehension. However, the dataset has ignored the fact that different readers may have different levels of understanding of the text, and only includes single-perspective question-answer pairs, leading to a lack of consideration of different perspectives. To tackle the above problem, we propose a novel multi-perspective SMRC dataset, called SciMRC, which includes perspectives from beginners, students and experts. Our proposed SciMRC is constructed from 741 scientific papers and 6,057 question-answer pairs. Each perspective of beginners, students and experts contains 3,306, 1,800 and 951 QA pairs, respectively. The extensive experiments on SciMRC by utilizing pre-trained models suggest the importance of considering perspectives of SMRC, and demonstrate its challenging nature for machine comprehension. © 2023, CC BY.

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PRIF: A Privacy-Preserving Interest-Based Forwarding Scheme for Social Internet of Vehicles Liehuang Zhu, Chuan Zhang, Chang Xu, Rixin Xu, Kashif Sharif are with

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arXiv 2018年

作者： Zhu, Liehuang Zhang, Chuan Xu, Chang Du, Xiaojiang Xu, Rixin Sharif, Kashif Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application Beijing Institute of Technology School of Computer Science and Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho Moscow Idaho United States

Recent advances in Socially Aware Networks (SANs) have allowed its use in many domains, out of which social Internet of vehicles (SIOV) is of prime importance. SANs can provide a promising routing and forwarding paradigm for SIOV by using interest-based communication. Though able to improve the forwarding performance, existing interest-based schemes fail to consider the important issue of protecting users' interest information. In this paper, we propose a PRivacy-preserving Interest-based Forwarding scheme (PRIF) for SIOV, which not only protects the interest information, but also improves the forwarding performance. We propose a privacy-preserving authentication protocol to recognize communities among mobile nodes. During data routing and forwarding, a node can know others' interests only if they are affiliated with the same community. Moreover, to improve forwarding performance, a new metric community energy is introduced to indicate vehicular social proximity. Community energy is generated when two nodes encounter one another and information is shared among them. PRIF considers this energy metric to select forwarders towards the destination node or the destination community. Security analysis indicates PRIF can protect nodes' interest information. In addition, extensive simulations have been conducted to demonstrate that PRIF outperforms the existing algorithms including the BEEINFO, Epidemic, and PRoPHET. Copyright © 2018, The Authors. All rights reserved.

关键词： Vehicles