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Overview of the Tenth Dialog System Technology Challenge: DSTC10

IEEE/ACM Transactions on Audio Speech and Language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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LightXML: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification

arXiv

引用

arXiv 2021年

作者： Jiang, Ting Wang, Deqing Sun, Leilei Yang, Huayi Zhao, Zhengyang Zhuang, Fuzhen SKLSDE and BDBC Lab Beihang University Beijing China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Academy for Multidisciplinary Studies Capital Normal University Beijing China

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML. Our code is available at http://***/kongds/LightXML. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Glycan immunogenicity prediction based on Graph neural network

Glycan immunogenicity prediction based on Graph neural netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Yu Wang, Hui Hou, Meijie Wang, Yaojun Bu, Dongbo Zhang, Chunming Huang, Chuncui Sun, Shiwei Institute of Computing Technology Chinese Academy of Sciences Key Lab of Intelligent Information Processing 6 Kexueyuan South Road Beijing100190 China Zhongke Big Data Academy Henan Zhengzhou450046 China University of Chinese Academy of Sciences 19 Yuquan Road Beijing100049 China Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing100083 China Phil Rivers Technology Beijing China

ISBN: (纸本)9781665401265

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%. © 2021 IEEE.

关键词： Message passing

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Iterative network pruning with uncertainty regularization for lifelong sentiment classification

arXiv

引用

arXiv 2021年

作者： Geng, Binzong Yang, Min Yuan, Fajie Wang, Shupeng Ao, Xiang Xu, Ruifeng University of Science and Technology of China China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Westlake University Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology CAS Harbin Institute of Technology Shenzhen China Tencent

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with uncertainty regularization in an iterative manner, IPRLS can adapt a single BERT model to work with continuously arriving data from multiple domains while avoiding catastrophic forgetting and interference. Specifically, we leverage an iterative pruning method to remove redundant parameters in large deep networks so that the freed-up space can then be employed to learn new tasks, tackling the catastrophic forgetting problem. Instead of keeping the old-tasks fixed when learning new tasks, we also use an uncertainty regularization based on the Bayesian online learning framework to constrain the update of old tasks weights in BERT, which enables positive backward transfer, i.e. learning new tasks improves performance on past tasks while protecting old knowledge from being lost. In addition, we propose a task-specific low-dimensional residual function in parallel to each layer of BERT, which makes IPRLS less prone to losing the knowledge saved in the base BERT network when learning a new task. Extensive experiments on 16 popular review corpora demonstrate that the proposed IPRLS method significantly outperforms the strong baselines for lifelong sentiment classification. For reproducibility, we submit the code and data at: https://***/siat-nlp/IPRLS. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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General Greedy De-bias Learning

arXiv

引用

arXiv 2021年

作者： Han, Xinzhe Wang, Shuhui Su, Chi Huang, Qingming Tian, Qi The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China SmartMore Beijing100085 China Huawei Cloud & AI Shenzhen518129 China

Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing with sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models' OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution (ID) and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge. Codes are available at https://***/GeraldHan/GGD. Copyright © 2021, The Authors. All rights reserved.

关键词： Curricula

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Zero-shot key information Extraction from Mixed-Style Tables: Pre-training on Wikipedia

Zero-shot Key Information Extraction from Mixed-Style Tables...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Qingping Yang Yingpeng Hu Rongyu Cao Hongwei Li Ping Luo Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China P.A.I. Ltd. Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665423991

Table, widely used in documents from various vertical domains, is a compact representation of data. There is always some strong demand to automatically extract key information from tables for further analysis. In addition, the set of keys that need to be extracted information is usually time-varying, which arises the issue of zero-shot keys in this situation. To increase the efficiency of these knowledge workers, in this study we aim to extract the values of a given set of keys from tables. Previous table-related studies mainly focus on relational, entity, and matrix tables. However, their methods fail on mixed-style tables, in which table headers might exist in any non-merged or merged cell, and the spatial relationships between headers and corresponding values are diverse. Here, we address this problem while taking mixed-style tables into account. To this end, we propose an end-to-end neural-based model, called information Extraction in Mixed-style Table (IEMT). IEMT first uses BERT to extract textual semantics of the given key and the words in each cell. Then, it uses multi-layer CNN to capture the spatial and textual interactions among adjacent cells. Furthermore, to improve the accuracy on zero-shot keys, we pre-train IEMT on a dataset constructed on 0.4 million tables from Wikipedia and 140 million triplets from Ownthink. Experiments with the fine-tuning step on 26,869 financial tables show that the proposed model achieves 0.9323 accuracy for zero-shot keys, obtaining more than 8% increase compared with the model without pre-training.

关键词： Conferences Semantics Bit error rate Encyclopedias Data models Internet Data mining

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Improving fake news detection by using an entity-enhanced framework to fuse diverse multimodal clues

arXiv

引用

arXiv 2021年

作者： Qi, Peng Cao, Juan Li, Xirong Liu, Huan Sheng, Qiang Mi, Xiaoyue He, Qin Lv, Yongbiao Guo, Chenyang Yu, Yingchao Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Institute of Artificial Intelligence Hebi China Key Lab of DEKE Renmin University of China Beijing China Zhengzhou University Zhengzhou China Hangzhou ZhongkeRuijian Technology Co. Ltd. Hangzhou China

Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies on this issue have made significant contributions to developing multimodal models, but they are defective in modeling the multimodal content sufficiently. Most of them only preliminarily model the basic semantics of the images as a supplement to the text, which limits their performance on detection. In this paper, we find three valuable text-image correlations in multimodal fake news: entity inconsistency, mutual enhancement, and text complementation. To effectively capture these multimodal clues, we innovatively extract visual entities (such as celebrities and landmarks) to understand the news-related high-level semantics of images, and then model the multimodal entity inconsistency and mutual enhancement with the help of visual entities. Moreover, we extract the embedded text in images as the complementation of the original text. All things considered, we propose a novel entity-enhanced multimodal fusion framework, which simultaneously models three cross-modal correlations to detect diverse multimodal fake news. Extensive experiments demonstrate the superiority of our model compared to the state of the art. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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Design of low-profile wide-band high-gain circularly polarized antenna based on metasurface

Design of low-profile wide-band high-gain circularly polariz...

引用

International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Wei Zhang Liang Zhang Xianliang Wu key lab of intelligent computing & signal processing ministry of education anhui university Anhui University Hefei China key lab of intelligent computing & signal processing ministry of education anhui university AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei Normal University Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper proposes a wide-band low-profile high-gain microstrip slot antenna loaded with a mushroom-type metasurface structure. By directly loading the metamaterial structure on the upper surface of the slot antenna, low profile loading characteristics are realized and the antenna bandwidth is expanded. And by inserting the metal line array in the metamaterial dielectric board to improve the antenna gain. Change the gap width through the metasurface element with corner cut to produce a phase difference and realize circularly polarized. Simulation test and analysis of antenna through HFSS simulation software. The results show that the antenna with this metasurface has a profile height of only 0.09×λ(λ =27 mm), a relative impedance band-width of 20.4% (S 11

关键词： Slot antennas Microstrip antennas Metasurfaces Metamaterials Microstrip Loaded antennas Gain

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Color Correction for Stereoscopic Images Based on Gradient Preservation

Color Correction for Stereoscopic Images Based on Gradient P...

引用

computing Conference, 2019

作者： Liu, Pengyu Niu, Yuzhen Chen, Junhao Shi, Yiqing College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

ISBN: (纸本)9783030228705

Color correction can eliminate the color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local color correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent color correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large color difference. © 2019, Springer Nature Switzerland AG.

关键词： Mapping

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Characterizing Multi-domain False News and Underlying User Effects on Chinese Weibo

arXiv

引用

arXiv 2022年

作者： Sheng, Qiang Cao, Juan Bernard, H. Russell Shu, Kai Li, Jintao Liu, Huan Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute for Social Science Research Arizona State University TempeAZ United States Department of Computer Science Illinois Institute of Technology ChicagoIL United States Computer Science and Engineering Arizona State University TempeAZ United States

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users - by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://***/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News. © 2022, CC BY-NC-ND.

关键词： Social networking (online)

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