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Euphemism Detection by Transformers and Relational Graph Attention network 3

Euphemism Detection by Transformers and Relational Graph Att...

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3rd Workshop on Figurative Language Processing, FigLang 2022, as part of EMNLP 2022

作者： Wang, Yuting Liu, Yiyi Zhang, Ruqing Fan, Yixing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781959429111

Euphemism is a type of figurative language broadly adopted in social media and daily conversations. People use euphemisms for politeness or to conceal what they are discussing. Euphemism detection is a challenging task because of its obscure and figurative nature. Even humans may not agree on if a word expresses euphemism. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and relational graph attention network in order to model the semantic and syntactic relations between the target words and the input sentence. The best performing method of ours reaches a macro F1 score of 84.0 on the euphemism detection dataset of the third workshop on figurative language processing shared task 2022. © 2022 Association for Computational Linguistics.

关键词： Semantics

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense 38

CausalDiff: Causality-Inspired Disentanglement via Diffusion...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Mingkun Bi, Keping Chen, Wei Chen, Quanrun Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS China School of Statistics University of International Business and Economics China

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/cas-AISafetyBasicResearchGroup/CausalDiff. © 2024 Neural information processing systems foundation. All rights reserved.

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Recent Advances in Generative Information Retrieval 24

Recent Advances in Generative Information Retrieval

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Tang, Yubao Zhang, Ruqing Sun, Weiwei Guo, Jiafeng de Rijke, Maarten CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China Shandong University Qingdao China University of Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400701726

Generative retrieval (GR) has witnessed significant growth recently in the area of information retrieval. Compared to the traditional "index-retrieve-then-rank" pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and applications of GR. We end by outlining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Information retrieval

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Locally smoothed neural networks 9

Locally smoothed neural networks

引用

9th Asian Conference on Machine Learning, ACML 2017

作者： Pang, Liang Lan, Yanyan Xu, Jun Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Convolutional Neural networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. © 2017 L. Pang†∗, Y. Lan†, J. Xu†, J. Guo† & X. Cheng†.

关键词： Convolution

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ICTNET at Trec 2019 Conversational Assistance Track 28

ICTNET at Trec 2019 Conversational Assistance Track

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28th Text REtrieval Conference, TREC 2019

作者： Hao, Changying Zhang, Yuanyuan Yang, Weifeng Zhao, Heng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper we report on our participation in the TREC 2019 Conversational Assistance Track which focuses on Conversational Information Seeking（CIS）namely understand the dialogue context and retrieve candidate response information from collections provided. We convert the CIS task into a standard information retrieval task and use both traditional IR model and neural IR model to rerank the baseline official evaluation results. We compare the results of models in two categories(four models in total), and give a summary for the solution of our work. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Information retrieval

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NeuInfer: Knowledge inference on N-ary facts 58

NeuInfer: Knowledge inference on N-ary facts

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

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

ISBN: (纸本)9781952148255

Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications. However, researchers have mainly poured attention to knowledge inference on binary facts. The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world. Therefore, this paper addresses knowledge inference on n-ary facts. We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s). We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference. It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s). Experimental results demonstrate the remarkable superiority of NeuInfer. © 2020 Association for Computational Linguistics

关键词： Knowledge graph

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MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification 29

MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Lear...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhao, Kailin Jin, Xiaolong Guan, Saiping Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attention. Such methods usually consist of a learner as the classifier and a meta learner for specializing the learner to different tasks. For the learner, learning rate is crucial to its performance. However, existing methods treat it as a hyper parameter and adjust it manually, which is time-consuming and laborious. Intuitively, for different tasks and neural network layers, the learning rates should be different and self-adaptive. For the meta learner, it requires a good generalization ability so as to quickly adapt to new tasks. Motivated by these issues, we propose a novel meta learning framework, called MetaSLRCL, for few-shot text classification. Specifically, we present a novel meta learning mechanism to obtain different learning rates for different tasks and neural network layers so as to enable the learner to quickly adapt to new training data. Moreover, we propose a task-oriented curriculum learning mechanism to help the meta learner achieve a better generalization ability by learning from different tasks with increasing difficulties. Extensive experiments on three benchmark datasets demonstrate the effectiveness of MetaSLRCL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Text processing

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A cross-lingual joint aspect/sentiment model for sentiment analysis 14

A cross-lingual joint aspect/sentiment model for sentiment a...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Lin, Zheng Jin, Xiaolong Xu, Xueke Wang, Yuanzhuo Wang, Weiping Cheng, Xueqi Minzhuang Road Beijing China Key Lab. of Network Data Science and Technology ICT CAS No. 6 Kexueyuan South Road Beijing China

ISBN: (纸本)9781450325981

Sentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, sentiment lexicons) of different languages are unbalanced in terms of quality and quantity, which arouses interests in cross-lingual sentiment analysis aiming at using the resources in a source language to improve sentiment analysis in a target language. Nevertheless, many existing cross-lingual related works rely on a certain machine translation system to directly adapt the labeled data from the source language to the target language, which usually suffers from inaccurate results generated by the machine translation system. On the other hand, most sentiment analysis studies focus on document-level sentiment classification that cannot solve the aspect dependency problem of sentiment words. For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system. To solve these problems, this paper develops a novel Cross-Lingual Joint Aspect/Sentiment (CLJAS) model to carry out aspect-specific sentiment analysis in a target language using the knowledge learned from a source language. Specifically, the CLJAS model jointly detects aspects and sentiments of two languages simultaneously by incorporating sentiments into a cross-lingual topic model framework. Extensive experiments on different domains and different languages demonstrate that the proposed model can significantly improve the accuracy of sentiment classification in the target language. Copyright 2014 ACM.

关键词： Sentiment analysis

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On the optimality of tape merge of two lists with similar size 27

On the optimality of tape merge of two lists with similar si...

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27th International Symposium on Algorithms and Computation, ISAAC 2016

作者： Li, Qian Sun, Xiaoming Zhang, Jialin CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959770262

The problem of merging sorted lists in the least number of pairwise comparisons has been solved completely only for a few special cases. Graham and Karp [18] independently discovered that the tape merge algorithm is optimal in the worst case when the two lists have the same size. Stockmeyer and Yao[28], Murphy and Paull[24], and Christen[6] independently showed when the lists to be merged are of size m and n satisfying m ≤ n ≤ ⌊3/2m⌋ + 1, the tape merge algorithm is optimal in the worst case. This paper extends this result by showing that the tape merge algorithm is optimal in the worst case whenever the size of one list is no larger than 1.52 times the size of the other. The main tool we used to prove lower bounds is Knuth's adversary methods [18]. In addition, we show that the lower bound cannot be improved to 1.8 via Knuth's adversary methods. We also develop a new inequality about Knuth's adversary methods, which might be interesting in its own right. Moreover, we design a simple procedure to achieve constant improvement of the upper bounds for 2m - 2 ≤ n ≤ 3m. © Qian Li, Xiaoming Sun, and Jialin Zhang.

关键词： Mergers and acquisitions

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Extended HMM and Ranking models for Chinese Spelling Correction 3

Extended HMM and Ranking models for Chinese Spelling Correct...

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3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2014

作者： Xiong, Jinhua Zhao, Qiao Hou, Jianpeng Wang, Qianbo Wang, Yuanzhuo Cheng, Xueqi Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences China

Spelling correction has been studied for many decades, which can be classified into two categories: (1) regular text spelling correction, (2) query spelling correction. Although the two tasks share many common techniques, they have different concerns. This paper presents our work on the CLP-2014 bake-off. The task focuses on spelling checking on foreigner Chinese essays. Compared to online search query spelling checking task, more complicated techniques can be applied for better performance. Therefore, we proposed a unified framework for Chinese essays spelling correction based on extended HMM and ranker-based models, together with a rule-based model for further polishing. Our system showed better performance on the test dataset. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All rights reserved.

关键词： Statistical tests

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