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arXiv 2022年

作者： Shang, Yu-Ming Huang, Heyan Mao, Xian-Ling School of Computer Science & Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or processing steps to make it easy to conduct. However, such a paradigm ignores the fact that the three elements of a triple are interdependent and indivisible. Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. Specifically, our model consists of a scoring-based classifier and a relation-specific horns tagging strategy. The former evaluates whether a token pair and a relation belong to a factual triple. The latter ensures a simple but effective decoding process. Extensive experimental results on two widely used datasets demonstrate that the proposed method performs better than the state-of-the-art baselines, and delivers consistent performance gain on complex scenarios of various overlapping patterns and multiple triples. © 2022, CC BY.

关键词： Extraction

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Distributed Dual Averaging for Composite Optimization

SSRN

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SSRN 2023年

作者： Wu, Ping Lu, Haolin Liu, Zhengyang Heyan, Huang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

In the context of a multi-agent network, we investigate the distributed online composite optimization problem where the optimization objective of each local agent is a combination of a convex cost function and a convex regularized function. To handle this problem, we introduce two distributed algorithms based on approximate dual averaging. The first online distributed composite dual averaging algorithm, under the gradient feedback, achieves a composite regret bound of $\mathcal{O}(\sqrt{T})$ and $\mathcal{O}(\ln T)$ for general convex and strongly convex regularized functions, respectively, across a total time horizon of $T$. Furthermore, the second one, relying exclusively on the values of cost function rather than the full information of gradient, attains a comparable composite regret bound against the first algorithm. To illustrate the effectiveness of our proposed algorithms, we present a plethora of numerical simulations for both the distributed regularized regression task and the distributed regularized classification task. © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Prediction or comparison: Toward interpretable qualitative reasoning

arXiv

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arXiv 2021年

作者： Ren, Mucheng Huang, Heyan Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Qualitative relationships illustrate how changing one property (e.g., moving velocity) affects another (e.g., kinetic energy) and constitutes a considerable portion of textual knowledge. Current approaches use either semantic parsers to transform natural language inputs into logical expressions or a "black-box" model to solve them in one step. The former has a limited application range, while the latter lacks interpretability. In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. In particular, we adopt neural network modules trained in an end-to-end manner to simulate the two reasoning processes. Experiments on two qualitative reasoning question answering datasets, QuaRTz and QuaRel, show our methods' effectiveness and generalization capability, and the intermediate outputs provided by the modules make the reasoning process interpretable. Copyright © 2021, The Authors. All rights reserved.

关键词： Kinetics

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Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension

Incorporating Option and Out-of-domain Knowledge for Multi-c...

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IEEE International Conference on cloud computing and Intelligence Systems (CCIS)

作者： Yuan Xu Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9781665441506

Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex matching networks to model the relationship among options, passage and question. However, these models obtain little improvement over the powerful Pre-trained language Models (PLMs). In this paper, we propose a simple method to incorporate option knowledge from PLMs and introduce out-of-domain knowledge by multi-task learning skillfully. Our approach obtains state-of-the-art results on Chinese multi-choice MRC dataset ReCO and also effectively improves the performance on C3.

关键词： Training cloud computing Conferences Computational modeling Multitasking Task analysis Intelligent systems

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Dynamic Regret with Unknown Delays

SSRN

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SSRN 2023年

作者： Wu, Ping Liu, Zhengyang Lu, Haolin Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

In this paper, we delve into the realm of delayed online convex optimization, addressing scenarios where unknown delays exist between the querying and revealing of feedback. Obtaining dynamic regret in this case poses challenges arising from the uncertainties associated with both comparator sequence and delay sequence. First, to handle the uncertainty of comparator sequence, we introduce an adaptive delayed algorithm, yielding an $\mathcal{O}(\max{\sqrt{(1+\Delta_T)D_T},(\hat{d}-1)\Delta_T})$ dynamic regret bound for arbitrary comparator sequences. Here, $\Delta_T$ represents the path-length of the comparator sequence, $\hat{d}$ denotes the maximum delay, and $D_T$ signifies the cumulative delay over the entire horizon. Our approach centers around employing a delayed meta algorithm to track parallel delayed expert algorithms with varying step sizes. To complement our theoretical analysis, we establish an $\Omega(\sqrt{(1+\Delta_T)D_T})$ lower bound. Furthermore, to address the uncertainty of delay sequence, we extend our adaptive algorithm to scenarios where the time horizon and cumulative delay are unknown, incorporating a delayed doubling trick tailored to the number of missing feedback instances, thereby achieving a comparable dynamic regret. Finally, numerical simulations are presented to validate the effectiveness of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Comparators (optical)

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Data Augmentation Under Scarce Condition for Neural Machine Translation

Data Augmentation Under Scarce Condition for Neural Machine ...

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IEEE International Conference on cloud computing and Intelligence Systems (CCIS)

作者： Dan Luo Shumin Shi Rihai Su Heyan Huang School of Computer Sciences and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

Neural Machine Translation (NMT) has achieved state-of-the-art performance depending on the availability of copious parallel corpora. However, for low-resource NMT task, the scarcity of training data will inevitably lead to poor translation performance. In order to relieve the dependence on scale of bilingual corpus and to cut down training time, we propose a novel data augmentation method named SMC under scarce condition that can Sample Monolingual Corpus containing difficult words only in back-translation process for Mongolian-Chinese (Mn-Ch) and English-Chinese (En-Ch) NMT. Inspired by work in curriculum learning, our approach takes into account the various difficulty-degree of the sample and the corresponding model capabilities. Experimental results show that our method improves translation quality respectively by up to 2.4 and 1.72 BLEU points over the baselines on En-Ch and Mn-Ch datasets while greatly reducing training time.

关键词： Training Data models Task analysis Training data information processing cloud computing Natural language processing

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Ask to Understand: Question Generation for Multi-hop Question Answering

arXiv

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arXiv 2022年

作者： Li, Jiawei Ren, Mucheng Gao, Yang Yang, Yizhe School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the "black-box" reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning logical route by decomposing multi-hop questions into simple single-hop sub-questions. In this paper, we propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of our proposed QG module, human evaluation further clarifies its interpretability quantitatively, and thorough analysis shows that the QG module could generate better sub-questions than QD methods in terms of fluency, consistency, and diversity. Copyright © 2022, The Authors. All rights reserved.

关键词： Decomposition

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

引用

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

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 State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Learning automata-based adaptive web services composition

Learning automata-based adaptive web services composition

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Guoqiang Li Dandan Song Lejian Liao Fuzhen Sun Jianguang Du School of informatics Linyi University Linyi China Language Information Processing & Cloud Computing Applications Beijing Institute of Technology Beijing China Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781479932801

Service-oriented computing is a widely adopted paradigm in real applications. Considering the continuous evolution of services, adaptive service composition has always been a major concern. It is a big challenge to adjust the composition to be optimal in real-time. In this paper, a learning automata-based approach is proposed to attack this problem. It consists of two important components: random environment and a learning automaton. The former can be mapped to the service's execution environment. The latter is responsible for the adaptation achievement using reward and penalty functions, while we take the service composition structures into account to compute the usefulness value of all services. At last, simulation study has shown that our approach is efficient to find the optimal (sub-optimal) composition.

关键词： Learning automata Quality of service Web services Adaptation models Automata Conferences Real-time systems

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MindLLM: Pre-training Lightweight Large language Model from Scratch, Evaluations and Domain applications

arXiv

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arXiv 2023年

作者： Yang, Yizhe Sun, Huashan Li, Jiawei Liu, Runheng Li, Yinghao Liu, Yuhang Gao, Yang Huang, Heyan School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Southeast Academy of Information Technology China

Large language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by developing increasingly large-scale models, there could be another branch to develop lightweight custom models that better serve certain domains, taking into account the high cost of training and deploying LLMs and the scarcity of resources. In this paper, we present MindLLM, a novel series of bilingual lightweight large language models, trained from scratch, alleviating such burdens by offering models with 1.3 billion and 3 billion parameters. A thorough account of experiences accrued during large model development is given, covering every step of the process, including data construction, model architecture, evaluation, and applications. Such insights are hopefully valuable for fellow academics and developers. MindLLM consistently matches or surpasses the performance of other open-source larger models on some public benchmarks. We also introduce an innovative instruction tuning framework tailored for smaller models to enhance their capabilities efficiently. Moreover, we explore the application of MindLLM in specific vertical domains such as law and finance, underscoring the agility and adaptability of our lightweight models. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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