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A Deep Cascade Model for Multi-Document Reading Comprehension

Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction  ...  with machine reading comprehension.  ...  The main contributions can be summarized as follow: • We propose a deep cascade learning framework to address the practical multi-document machine reading comprehension task, which considers both the effectiveness  ... 
doi:10.1609/aaai.v33i01.33017354 fatcat:lo52vbvoaffjrbbcqloic2kxny

A Deep Cascade Model for Multi-Document Reading Comprehension [article]

Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
2018 arXiv   pre-print
To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction  ...  with machine reading comprehension.  ...  The main contributions can be summarized as follow: • We propose a deep cascade learning framework to address the practical multi-document machine reading comprehension task, which considers both the effectiveness  ... 
arXiv:1811.11374v1 fatcat:3jtulmdoyjfd3ltxjrnyoy7liu

Deep Understanding based Multi-Document Machine Reading Comprehension

Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang
2022 ACM Transactions on Asian and Low-Resource Language Information Processing  
To overcome this deficiency, we propose a deep understanding based model for multi-document machine reading comprehension.  ...  Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings  ...  ACKNOWLEDGMENTS This work is supported by the National Natural Science Foundation of China (No.61572120) and the Fundamental Research Funds for the Central Universities (No.N181602013).  ... 
doi:10.1145/3519296 fatcat:lledqfttpnbs5b3eftrtzha6sm

Multi-Mention Learning for Reading Comprehension with Neural Cascades [article]

Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski
2018 arXiv   pre-print
Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur.  ...  In this work, we take a different approach by constructing lightweight models that are combined in a cascade to find the answer.  ...  We also thank Minjoon Seo, Luheng He, Dipanjan Das, Michael Collins, Chris Clark and Luke Zettlemoyer for helpful discussions and feedback. Finally, we thank anonymous reviewers for their comments.  ... 
arXiv:1711.00894v2 fatcat:62lopz3wnveujo5zt7orps5qf4

RikiNet: Reading Wikipedia Pages for Natural Question Answering [article]

Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Nan Duan
2020 arXiv   pre-print
In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering.  ...  RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor.  ...  There are several recently proposed deep learning approaches for multi-passage reading comprehension. propose DrQA which contains a document retriever and a document reader (DocReader).  ... 
arXiv:2004.14560v1 fatcat:ket5dbtm3zetzkllkvuyiuihqq

DP-LinkNet: A convolutional network for historical document image binarization

2021 KSII Transactions on Internet and Information Systems  
The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net.  ...  Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin.  ...  [43] propose a deep learning framework to infer the probabilities of text regions through a multi-resolution attentional model, which is then fed into a convolutional conditional random field (ConvCRF  ... 
doi:10.3837/tiis.2021.05.011 fatcat:3g27oo6j6racdb7xcl42z3sgme

Translucent Answer Predictions in Multi-Hop Reading Comprehension

G P Shrivatsa Bhargav, Michael Glass, Dinesh Garg, Shirish Shevade, Saswati Dana, Dinesh Khandelwal, L Venkata Subramaniam, Alfio Gliozzo
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Research on the task of Reading Comprehension style Question Answering (RCQA) has gained momentum in recent years due to the emergence of human annotated datasets and associated leaderboards, for example  ...  In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop  ...  While this strategy was shown to be effective for some factoid questions, it is not sufficient for multi-hop reading comprehension.  ... 
doi:10.1609/aaai.v34i05.6272 fatcat:p3difydtl5f7jaeqqdcdduepma

MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering [article]

Tolgahan Cakaloglu, Xiaowei Xu
2019 arXiv   pre-print
In this paper, we devise a multi-resolution neural network(MRNN) to leverage the whole hierarchy of representations for document retrieval.  ...  Recently deep neural networks have been used to rank search results in response to a query.  ...  large reading comprehension dataset that is built with 100, 000+ questions.  ... 
arXiv:1911.00964v1 fatcat:vxhhe4fbwrhcxbgdit36mwomqy

ivadomed: A Medical Imaging Deep Learning Toolbox

Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Marie-Helene Bourget, Anthime Bucquet, Joseph Cohen, Julien Cohen-Adad
2021 Journal of Open Source Software  
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.  ...  The package includes APIs, commandline tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling.  ...  their useful contributions, and Guillaume Dumas for proof-reading the manuscript.  ... 
doi:10.21105/joss.02868 fatcat:rotfmznodjbzhpbma2dmne2xfi

A Compact Survey on Event Extraction: Approaches and Applications [article]

Qian Li, Jianxin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu
2021 arXiv   pre-print
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.  ...  This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.  ...  Machine Reading Comprehension Task The machine reading comprehension model [87] , [88] , [89] can understand a piece of text in natural language and answer questions about it.  ... 
arXiv:2107.02126v5 fatcat:ncnlgrssqbcfpekvm4rrmrd6gi

Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension

Chia-Hsuan Lee, Szu-Lin Wu, Chi-Liang Liu, Hung-yi Lee
2018 Interspeech 2018  
Reading comprehension has been widely studied.  ...  In this paper, we propose a new listening comprehension task -Spoken SQuAD.  ...  Model The SQA system is the cascade of an ASR module and reading comprehension module. In this section, we first briefly introduce the reading comprehension models we used.  ... 
doi:10.21437/interspeech.2018-1714 dblp:conf/interspeech/LeeWLL18 fatcat:mpfw5zqr6fdlpifhauthif4ml4

A multi-channel convolutional neural network approach to automate the citation screening process

Raymon van Dinter, Cagatay Catal, Bedir Tekinerdogan
2021 Applied Soft Computing  
It was shown that for 18 out of 20 review datasets, the proposed method achieved significant workload savings of at least 10%, while in several cases, our model yielded a statistically significantly better  ...  A Multi-Channel Convolutional Neural Network (CNN) is proposed, which can automatically classify a given set of citations.  ...  The model loads the source document using different kernel sizes, producing a Multi-Channel CNN that reads the text with various n-gram sizes [10] .  ... 
doi:10.1016/j.asoc.2021.107765 fatcat:n45r2typira3fnbfjl5x23ijsm

DeepCas: an End-to-end Predictor of Information Cascades [article]

Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei
2016 arXiv   pre-print
Our results also provide interesting implications for cascade prediction in general.  ...  Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades.  ...  Once we have such a "document" assembled, deep learning techniques for text data could be applied in a similar way here.  ... 
arXiv:1611.05373v1 fatcat:iqz3l7drxnf6hmgfmh7moa3aei

Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge [article]

Zhuosheng Zhang, Junlong Li, Hai Zhao
2021 arXiv   pre-print
We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension.  ...  Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions.  ...  In this work, we propose a pivot-oriented deep selection model (PoDS) for dialogue comprehension.  ... 
arXiv:2102.05474v1 fatcat:mgze2s6ypbf6zoucrxqmatnroq

MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

Cheng-Lin Wu, Hsun-Ping Hsieh, Jiawei Jiang, Yi-Chieh Yang, Chris Shei, Yu-Wen Chen
2022 Applied Sciences  
To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content  ...  The attention mechanism developed in the model can provide explainability for social or psychological analysis.  ...  In addition, no comprehensive framework captures the dynamics of multi-modal attributes and preserves the model explainability.  ... 
doi:10.3390/app12010453 fatcat:ryfj6ggqjrejxhzgllxavwatye
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