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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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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

arXiv

引用

arXiv 2023年

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application needs. To overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of LLMs. For such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts. Comprehensive experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Unsupervised Question Answering via Answer Diversifying 29

Unsupervised Question Answering via Answer Diversifying

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Nie, Yuxiang Huang, Heyan Chi, Zewen Mao, Xian-Ling 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 Beijing Institute of Technology Southeast Academy of Information Technology Fujian China

Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard named entity (NE) as the only answer type, which ignores the high diversity of answers in the real world. To tackle this problem, we propose a novel unsupervised method by diversifying answers, named DiverseQA. Specifically, the proposed method is composed of three modules: data construction, data augmentation and denoising filter. Firstly, the data construction module extends the extracted named entity into a longer sentence constituent as the new answer span to construct a QA dataset with diverse answers. Secondly, the data augmentation module adopts an answer-type dependent data augmentation process via adversarial training in the embedding level. Thirdly, the denoising filter module is designed to alleviate the noise in the constructed data. Extensive experiments show that the proposed method outperforms previous unsupervised models on five benchmark datasets, including SQuADv1.1, NewsQA, TriviaQA, BioASQ, and DuoRC. Besides, the proposed method shows strong performance in the few-shot learning setting. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Natural language processing systems

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The Span-Based Transformer Classifier for Named Entity Recognition

The Span-Based Transformer Classifier for Named Entity Recog...

引用

International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Li, Yangfan Sun, Xin Li, Hongao Yang, Kaige Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Southeast Academy of Information Technology Fujian Putian351100 China

ISBN: (纸本)9789811694912

Named entity recognition is a basic task in the field of natural language processing. The existing named entity recognition model has the problem of poor recognition effect on specific domain data sets. Aiming at the inconsistency of sequence labeling caused by the shortcomings of the traditional BIO (B-begin, I-inside, O-outside) annotation method, a named entity recognition model based on transformer is proposed, which uses the feature extraction of encoder in transformer and the labeling method of limited interval. Firstly, the BERT pre-training model is used to perform word-level dynamic vector representation of the input text. Then the vector representation of the text is intercepted by the span-based labeling method, and the feature of text and candidate named entity is extracted by self-attention mechanism. The results of experiment on the dataset in the industrial informatization field show the effectiveness of the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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A Denoising Method for Distant Supervised Relation Extraction Based on Deep Clustering

A Denoising Method for Distant Supervised Relation Extractio...

引用

International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Li, Hongao Sun, Xin Yang, Kaige Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Southeast Academy of Information Technology Fujian Putian351100 China

ISBN: (纸本)9789811694912

Distant supervision for relation extraction uses an external knowledge base as supervision signals to automatically label corpus and has attracted more and more attention. However, this method has an ideal hypothesis that all instances containing the same entity pairs represent the same relation, which will lead to a lot of noisy data and affect the training effect of classifier. Aiming at the noise problem of distant supervised dataset, we propose a denoising method based on deep clustering. First, the deep clustering method is used to train the text features and clustering centers, and the effect of clustering is improved by using the information of mask entity pairs. Then high quality training samples are obtained by removing the noisy data, so as to achieve the effect of denoising. Experimental results show that the proposed model can effectively improve the performance of relation extraction by comparing with the traditional denoising methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge based systems

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Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension

arXiv

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arXiv 2024年

作者： Wang, Chenxu Jian, Ping Yang, Zhen 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 Institute of Technology Beijing China

Logical reading comprehension is a challenging task that involves understanding the underlying semantics of text and applying reasoning to deduce the correct answer. Prior researches have primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. However, previous work constructing chain-of-thought rationales concentrates solely on analyzing correct options, neglecting the incorrect alternatives. Addtionally, earlier efforts on data augmentation by altering contexts rely on rule-based methods, which result in generated contexts that lack diversity and coherence. To address these issues, we propose a Premise-Oriented Data Augmentation (PODA) framework. This framework can generate CoT rationales including analyses for both correct and incorrect options, while constructing diverse and high-quality counterfactual contexts from incorrect candidate options. We integrate summarizing premises and identifying premises for each option into rationales. Subsequently, we employ multi-step prompts with identified premises to construct counterfactual context. To facilitate the model’s capabilities to better differentiate the reasoning process associated with each option, we introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples. Experimental results on three representative LLMs demonstrate that our method can improve the baselines substantially across two challenging logical reasoning benchmarks (ReClor and LogiQA 2.0). © 2024, CC BY.

关键词： Contrastive Learning

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Capturing Global Structural information in Long Document Question Answering with Compressive Graph Selector Network

Capturing Global Structural Information in Long Document Que...

引用

2022 Conference on Empirical Methods in Natural language processing, EMNLP 2022

作者： Nie, Yuxiang Huang, Heyan Wei, Wei 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 Beijing Institute of Technology Southeast Academy of Information Technology China Huazhong University of Science and Technology China

Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. The proposed model mainly focuses on the evidence selection phase of long document question answering. Specifically, it consists of three modules: local graph network, global graph network and evidence memory network. Firstly, the local graph network builds the graph structure of the chunked segment in token, sentence, paragraph and segment levels to capture the short-term dependency of the text. Secondly, the global graph network selectively receives the information of each level from the local graph, compresses them into the global graph nodes and applies graph attention to the global graph nodes to build the long-range reasoning over the entire text in an iterative way. Thirdly, the evidence memory network is designed to alleviate the redundancy problem in the evidence selection by saving the selected result in the previous steps. Extensive experiments show that the proposed model outperforms previous methods on two datasets. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

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Dynamic Regret with Unknown Delays

SSRN

引用

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

We study the problem of online convex optimization in dynamic environments with unknown and arbitrary delays. Without delays, existing work has achieved the optimal $\Theta(\sqrt{(1+\Delta_T)T})$ regret bound for any given sequence of comparators, where $T$ is the time horizon and $\Delta_T$ is the path-length of the comparator sequence. However, their algorithm assumes that the loss function can be revealed immediately to the agent after making the decision. In this paper, we propose a novel algorithm to tackle the delayed setting. We show that it enjoys $\mathcal{O}(\max\{\sqrt{(1+\Delta_T)D_T},(\hat{d}-1)\Delta_T\})$ regret bound, where $\hat{d}$ is the maximum delay and $D_T$ is the cumulative delay over the whole horizon. To complement our algorithm, we also establish an $\Omega(\sqrt{(1+\Delta_T)D_T})$ lower bound. The basic idea is to employ a delayed meta algorithm to track parallel delayed expert algorithms with varying step sizes. We further extend our algorithm to the case that we have no idea about the time horizon and cumulative delay using a novel doubling trick. Finally, numerical simulations are provided to illustrate the validity of our theoretical results. © 2023, The Authors. All rights reserved.

关键词： Comparators (optical)

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Distributed Dual Averaging for Composite Optimization

SSRN

引用

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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Dynamic Regret with Unknown Delays

SSRN

引用

SSRN 2023年

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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