Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen , AAAI, 2019. The common attention functions are as follows: Inner product function: . Code is available at https://github.com/lironui/Linear-Attention-Mechanism. In order to improve the performance of feature extraction by convolutional neural network, an attention mechanism module is embedded behind the pooling layer of . al. The Attention . The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed- forward network. A Tumor Named Entity Recognition Model Based on Pre-trained Language Model and Attention Mechanism XinTao a,RenyuanLiu andXiaobingZhoua aSchool of Information Science and Engineering, Yunnan University, Kunming 650091, P.R. An Unsupervised Model with Attention Autoencoders for Question Retrieval Minghua Zhang, Yunfang Wu, AAAI, 2018. Even though the paper itself mentions the word “attention” scarcely (3 times total in 2 consecutive lines!!) Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms ubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia,EMNLP, 2018. Multi-grained Attention Network for Aspect-Level Sentiment Classification Feifan Fan, Yansong Feng, Dongyan Zhao, EMNLP, 2018. Multi-head is the concept of adding dimensions or subspaces to the self-attention mechanism to retrieve more meaning, in the paper they used 8 heads. The paper itself is very clearly written, but the conventional wisdom has been that it is quite difficult to implement correctly. This form of attention mechanism is known as "hard-attention". In the above figure, multi head attention is to do the scaled dot product attention process h times, and then merge the output. Improving Neural Fine-Grained Entity . Attention not only tells where to focus, it also improves the representation of interests. Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism Wei Qian, Cong Fu, Yu Zhu, Deng Cai, Xiaofei He, IJCAI, 2018. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. However, with the proposed 2D form attention mechanism in this work, the rectification step becomes unnecessary. Found inside – Page 352We will use the attention mechanism detailed in the paper, Neural Machine Translation by Learning to Jointly Align and Translate, Bahdanau, Cho, and Bengio, ... How Much Attention Do You Need? Found inside – Page 26With the development of neural networks, the paper [11] proposed two global and local Attention mechanisms, which showed the expansion of attention in RNN ... Adaptive Co-Attention Network for Named Entity Recognition in Tweets Qi Zhang, Jinlan Fu, Xiaoyu Liu, Xuanjing Huang, AAAI, 2018. The paper aimed to improve the sequence-to-sequence model in machine translation by aligning the decoder with the relevant input sentences and implementing Attention. Effective Approaches to Attention-based Neural Machine Translation This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. In this paper, our attention module contains two branches, one is the trunk branch to obtain feature F p , and the other is the mask branch which integrates LBP features . Cheng et al, in their paper named "Long Short-Term Memory-Networks for Machine Reading", defined self-Attention as the mechanism of relating different positions of a single sequence or sentence in order to gain a more vivid representation. Attention mechanisms have become an integral part of compelling sequence modeling and transduc-tion models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 16]. attention mechanisms do not work. Indeed, they have demonstrated both hard-attention and soft-attention mechanisms in their paper. Found inside – Page 79The combination is done using an attention mechanism called “sentinel mixture architecture.” This paper proposes a fully data-driven approach to DST that ... Found inside – Page 311Therefore, this paper introduces the multi-head attention mechanism and hopes to learn the user's interest features in different contexts. Found inside – Page 99In addition, Baron-Cohen (1995) claims that Theory of Mind is also developed through a shared attention mechanism, where children combine information from ... In this paper, we propose a self-supervised graph attention network (SuperGAT), an improved graph attention model for noisy graphs. Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions Zhipeng Chen, Yiming Cui, Wentao Ma, Shijin Wang, Guoping Hu, AAAI, 2019. COMBINING CHARACTER AND WORD INFORMATION IN NEURAL MACHINE TRANSLATION USING A MULTI-LEVEL ATTENTION Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, Jiajun Chen,NAACL, 2018. It combines the In this paper, we propose an attention-based deep neural &&e_{i,j} = a(s_{i-1 . Attention-via-Attention Neural Machine Translation Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, Huajun Chen, EMNLP, 2018. Attention mastered! Bahdanau et al. In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. Papers With Code is a free resource with all data licensed under CC-BY-SA. However, it is difficult to analyze the decision-making of the agent, i.e., the reasons it selects the action acquired by learning . Work fast with our official CLI. the attention mechanism. Deriving Machine Attention from Human Rationales Yujia Bao, Shiyu Chang, Mo Yu, Regina Barzilay, EMNLP, 2018. The attention mechanism introduced in this paper usually refers to focused attention except for special statements. This paper presents a novel attention-based graph neural network that introduces an attention mechanism in the word-represented features of a node together incorporating the neighbors' attention in the . WECA：A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang, EMNLP, 2018. In our paper, we show that the Transformer outperforms both recurrent and convolutional models on academic English to German and . Therefore, this paper proposes a novel framework for student performance prediction by making full use of both student behavior features and exercise features and combining the attention mechanism with the knowledge tracing model. Found inside – Page 2777However, little attention has been paid to adaptively fuse multi-level information under visual attention mechanism. In our paper, a Selective Information ... Besides, attention mechanism has also made progress in QA systemsChen et al. Found inside – Page 5The paper by Hommel also suggests a role for attention in binding visual features into integrated object configurations , called " object files ” by ... In order to solve the above problems, a novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper. The paper further refined the self-attention layer by adding a mechanism called "multi-headed" attention. The effect enhances the important parts of the input data and fades out the rest—the thought being that the network should devote more computing power to that small but important part of the data. (2014) proposed applying the attention mechanism on neural network machine translation for the first time. Intro. Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu,ACL, 2018. How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen, NAACL, 2018. The utility of the attention mechanism can be seen in the language translation task [2] where it is inefficient to represent an Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN Jingqiang Chen, Hai Zhuge, EMNLP, 2018. Efficient Large-Scale Neural Domain Classification with Personalized Attention Young-Bum Kim, Dongchan Kim, Anjishnu Kumar, Ruhi Sarikaya, ACL, 2018. Background: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. Second, the comparative maps are obtained by carrying out center-surround difference operator. Higher-Order Syntactic Attention Network for Longer Sentence Compression Hidetaka Kamigaito, Katsuhiko Hayashi, Tsutomu Hirao, Masaaki Nagata, NAACL, 2018. Found inside – Page 511.1 Contribution In this paper, a multi-model fusion brain tumor automatic segmentation algorithm based on attention mechanism is designed. A typical attention model on se-quential data has been proposed by Xu et al . Deep reinforcement learning (DRL) has great potential for acquiring the optimal action in complex environments such as games and robot control. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances . Found inside – Page 452In this paper, we also employ attention mechanism both in the tackling of OOVs and knowledge selections. In recent years, knowledge incorporation is found ... (&) Also, referred to as "intra-attention" in Cheng et al., 2016 and some other papers. Learn more . Found inside – Page 3555 Conclusion In this paper, we propose a model that uses a multi-dimensional dual attention mechanism. The attention mechanism is used at both the lexical ... Found inside – Page 95In short, following are our contributions in this paper: 1. We propose an appropriate attention mechanism incorperated with the traditional Encoder-Decoder ... Found inside – Page 350This paper will quantitatively evaluate the effect of different attention mechanism models on deepfakes detection through experiments. Probably it’s just me but the explanation given in the paper and the diagrams that came with it left a lot to the imagination. the term has caught on. Convolutions for Space. This paper caught on, and Attention Mechanisms then became common in NLP tasks based on neural networks such as RNN/CNN. That means the attention mechanism is meaningful in this paper. Accelerating Neural Transformer via an Average Attention Network Biao Zhang, Deyi Xiong, jinsong su, ACL, 2018. Why Self-Attention? Broadly, the whole process can be divided into four steps: Hence the hidden state for the jᵗʰ input hⱼ is the concatenation of jᵗʰ hidden states of forward and backward RNNs. In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs. Found insideThis two-volume set LNAI 12163 and 12164 constitutes the refereed proceedings of the 21th International Conference on Artificial Intelligence in Education, AIED 2020, held in Ifrane, Morocco, in July 2020.* The 49 full papers presented ... Found inside – Page 407In these tasks, the attention mechanism reflects the excellent key area ... In this paper, we designed an attention-based text detection model that can be ... Linear (or efficient) attention mechanisms (Katharopoulos Learning Universal Sentence Representations with Mean-Max Attention Autoencoder Minghua Zhang, Yunfang Wu, Weikang Li, Wei Li, EMNLP, 2018. Hierarchical Attention Flow for Multiple-Choice Reading Comprehension, Dual Attention Network for Product Compatibility and Function Satisfiability Analysis, Improving Neural Fine-Grained Entity Typing with Knowledge Attention, Improving Review Representations with User Attention and Product Attention for Sentiment Classification, Mention and Entity Description Co-Attention for Entity Disambiguation, Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification, A Question-Focused Multi-Factor Attention Network for Question Answering, RNN-Based Sequence-Preserved Attention for Dependency Parsing, Adaptive Co-Attention Network for Named Entity Recognition in Tweets, Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention, Multi-Attention Recurrent Network for Human Communication Comprehension, Hierarchical Recurrent Attention Network for Response Generation, Word Attention for Sequence to Sequence Text Understanding, DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding, Attention-Based Belief or Disbelief Feature Extraction for Dependency Parsing, An Unsupervised Model with Attention Autoencoders for Question Retrieval, Deep Semantic Role Labeling with Self-Attention, Event Detection via Gated Multilingual Attention Mechanism, Neural Knowledge Acquisition via Mutual Attention between Knowledge Graph and Text, Syntax-Directed Attention for Neural Machine Translation, Attend and Diagnose: Clinical Time Series Analysis Using Attention Models, Attention-Based Transactional Context Embedding for Next-Item Recommendation, Attention-via-Attention Neural Machine Translation, Visual Attention Model for Name Tagging in Multimodal Social Media, Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings, Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network, Cold-Start Aware User and Product Attention for Sentiment Classification, Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering, Multi-Input Attention for Unsupervised OCR Correction, How Much Attention Do You Need? This paper exam-ines two simple and effective classes of at- Found inside – Page iiThe sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented ... A MIXED HIERARCHICAL ATTENTION BASED ENCODER-DECODER APPROACH FOR STANDARD TABLE SUMMARIZATION Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, Shreyas Shetty, NAACL, 2018. Attention-via-Attention Neural Machine Translation Shenjian Zhao, Zhihua Zhang, AAAI, 2018. Found inside – Page 598Exploration of Different Attention Mechanisms on Medical Image ... In this paper, we explore the implementation and discrimination of four specific ... Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism Nafise Sadat Moosavi, Michael Strube, EMNLP, 2018. The recent surge in interest in devices such as explicitly modeled attention mechanisms fall into the realm of explainability of neural . Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention ClusteringRui Zhang, Cicero Nogueira dos Santos, Michihiro Yasunaga, Bing Xiang, Dragomir Radev, ACL, 2018. Listen, Think and Listen Again: Capturing Top-down Auditory Attention for Speaker-independent Speech Separation Jing Shi, Jiaming Xu, Guangcan Liu, Bo Xu, IJCAI, 2018. Add a Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya,EMNLP, 2018. It is based solely on attention mechanisms: i.e., without recurrence or convolutions. Thus, it is able to model arbitrarily long contexts and . Download PDF Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. In this paper, we propose a knowledge graph embedding method with attention mechanism named TransAt (Transla-tion with Attention). Found insideThe main theme of the book is the attention processes of vision systems and it aims to point out the analogies and the divergences of biological vision with the frameworks introduced by computer scientists in artificial vision. Since . Hard Non-Monotonic Attention for Character-Level Transduction Shijie Wu, Pamela Shapiro, Ryan Cotterell, EMNLP, 2018. A Genre-Aware Attention Model to Improve the Likability Prediction of Books Suraj Maharjan, Manuel Montes-y-Gómez, Fabio A. González, Thamar Solorio, EMNLP, 2018. Found inside – Page 692Based on this, in order to embed sentimental information into the attention mechanism, this paper proposes to embed part of speech into the self-attention ... Found inside – Page 388... the model of this paper is most closely related to the HANs model, and the HANs model represents a sentence with a hierarchical attention mechanism. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. In this paper, we propose a new network module, named "Convolutional Block Attention Module". For details about training look into Appendix B of the paper. Attention-Based Belief or Disbelief Feature Extraction for Dependency Parsing Haoyuan Peng, Lu Liu, Yi Zhou, Junying Zhou, Xiaoqing Zheng, AAAI, 2018. Multi-Head Attention with Disagreement Regularization Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, Tong Zhang, EMNLP, 2018. Edit social preview, In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs. Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning Xin Wang, Yuan-Fang Wang, William Yang Wang, NAACL, 2018. Click To Get Model/Code. China Abstract Named entity recognition is to recognize the mention of a certain thing or concept in a text in natural Found inside – Page 78This paper explores using the attention mechanism to explain the results of the machine learning model, and find out which parts of the text are more ... This book on finite group theory is a great resource for both undergraduate and graduate students in the Mathematical sciences. Document-Level Neural Machine Translation with Hierarchical Attention Networks Lesly Miculicich Werlen, Dhananjay Ram, Nikolaos Pappas, James Henderson, EMNLP, 2018. The attention mechanism in deep learning simulates the attention characteristics of the human brain, which can be understood as always paying Attention to more important information. Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke, EMNLP, 2018. A Hierarchical Neural Attention-based Text Classifier Koustuv Sinha, Yue Dong, Jackie Chi Kit Cheung, Derek Ruths, EMNLP, 2018. All there remains is to use the context vector cₜ we worked so hard to compute, along with the previous hidden state of the decoder s ₜ-₁ and the previous output yₜ-₁and use all of them to compute the new hidden state and output of the decoder: sₜ and yₜ respectively. attention (Bahdanau et al.,2015;Vaswani et al.,2017) has emerged as a popular approach to do so, but the costly memory requirement of self-attention hinders its application to long sequences and multidimensional data such as images2. Aspect Term Extraction with History Attention and Selective Transformation Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang, IJCAI, 2018. [30] went one step further to use an attention mechanism in the caption generation process. Rasa for beginners and how to build a basic chatbot. Character-Level Language Modeling with Deeper Self-Attention Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, Llion Jones , AAAI, 2019. A Granular Analysis of Neural Machine Translation Architectures, Accelerating Neural Transformer via an Average Attention Network, Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment, Document Modeling with External Attention for Sentence Extraction, Efficient Large-Scale Neural Domain Classification with Personalized Attention, Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering, Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention, Sparse and Constrained Attention for Neural Machine Translation, Cross-Target Stance Classification with Self-Attention Networks, A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification, Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention, Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism, Surprisingly Easy Hard-Attention for Sequence to Sequence Learning, Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates, A Hierarchical Neural Attention-based Text Classifier, Supervised Domain Enablement Attention for Personalized Domain Classification, Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network, Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging, Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation, Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms, Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention, Neural Related Work Summarization with a Joint Context-driven Attention Mechanism, Deriving Machine Attention from Human Rationales, Attention-Guided Answer Distillation for Machine Reading Comprehensio, Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction, Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention, WECA：A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition, Multi-Head Attention with Disagreement Regularization, Document-Level Neural Machine Translation with Hierarchical Attention Networks, Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation, Training Deeper Neural Machine Translation Models with Transparent Attention, A Genre-Aware Attention Model to Improve the Likability Prediction of Books, Multi-grained Attention Network for Aspect-Level Sentiment Classification, Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification, Contextual Inter-modal Attention for Multi-modal Sentiment Analysis, A Visual Attention Grounding Neural Model for Multimodal Machine Translation, Phrase-level Self-Attention Networks for Universal Sentence Encoding, Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks, Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN, Why Self-Attention? task. In 2017, Google's machine translation team used self-attention mechanisms . Looking forward to your comments and suggestions. It has also recently been applied in several domains in machine learning. . In this paper, we pro-pose an attention mechanism to enforce the model to attend to the important part of a sentence, in re-sponse to a specic aspect. Any attention effect. Because this paper adopts a dual attention mechanism, it describes that attention strategies should be adopted for visual and textual features respectively in the process of generation. We first explain attention mechanism, sequence-to-sequence model without and with . Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang, IJCAI, 2018. Finally, a recent work in stock price pre-dictionQin et al. Higher the value, higher the impact of hⱼ on the context for time t. We are nearly there! Hierarchical Attention Flow for Multiple-Choice Reading Comprehension Haichao Zhu, Furu Wei, Bing Qin, Ting Liu AAAI, 2018. Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction Jinhua Du, Jingguang Han, Andy Way, Dadong Wan, EMNLP, 2018. Shi et al. Found inside – Page 123The NLOOK attention model was chosen because, as described in [14], ... The mechanism proposed in this paper, on the other hand, can work using real images ... Found inside – Page 56This paper is organized as the following. ... In this paper, authors have shown that attention mechanism has been proven as a better way of doing NMT. This tutorial would be the first step for taking deep dive into the field. A Targeted Evaluation of Neural Machine Translation Architectures, Hard Non-Monotonic Attention for Character-Level Transduction, Modeling Localness for Self-Attention Networks, Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences, Learning Universal Sentence Representations with Mean-Max Attention Autoencoder, A Co-attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness, Interpretable Emoji Prediction via Label-Wise Attention LSTMs, Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference, Linguistically-Informed Self-Attention for Semantic Role Labeling, Discourse-Aware Neural Rewards for Coherent Text Generation, A MIXED HIERARCHICAL ATTENTION BASED ENCODER-DECODER APPROACH FOR STANDARD TABLE SUMMARIZATION, COMBINING CHARACTER AND WORD INFORMATION IN NEURAL MACHINE TRANSLATION USING A MULTI-LEVEL ATTENTION, Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization, Generating Topic-Oriented Summaries Using Neural Attention, Higher-Order Syntactic Attention Network for Longer Sentence Compression, How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues, Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis, Self-Attention with Relative Position Representations, Target Foresight Based Attention for Neural Machine Translation, Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning, Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation, Medical Concept Embedding with Time-Aware Attention, Attention-Fused Deep Matching Network for Natural Language Inference, Multi-modal Sentence Summarization with Modality Attention and Image Filtering, Code Completion with Neural Attention and Pointer Networks, Aspect Term Extraction with History Attention and Selective Transformation, Feature Enhancement in Attention for Visual Question Answering, Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention, Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism, Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling, Listen, Think and Listen Again: Capturing Top-down Auditory Attention for Speaker-independent Speech Separation, Multiway Attention Networks for Modeling Sentence Pairs, Get The Point of My Utterance! As you might have guessed already, an attention mechanism assigns a probability to each vector in memory and context vector is the vector that has the maximum probability assigned to it. We would also employ Transformer Architecture, Scaled Dot Product Attention, and Multi-Head Attention. Attention is the important ability to flexibly control limited computational resources. Through the spatiotemporal . Multi attention mechanism. Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning. Vanilla Neural Nets. Learning Towards Effective Responses with Multi-Head Attention Mechanism, Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains, Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks, Densely Connected CNN with Multi-scale Feature Attention for Text Classification, Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks, Commonsense Knowledge Aware Conversation Generation with Graph Attention, To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression, An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss, Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding, Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction, Deep Metric Learning by Online Soft Mining and Class-Aware Attention, Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering, Character-Level Language Modeling with Deeper Self-Attention, Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions. Linguistically-Informed Self-Attention for Semantic Role Labeling Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum, EMNLP, 2018. However, there has been little work exploring useful architectures for attention-based NMT. In vanilla attention mechanisms (Bah- In this paper, we propose a Parallel, Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency. Attention is the important ability to flexibly control limited computational resources. Attention mechanism for sequence modelling was first introduced in the paper: Neural Machine Translation by jointly learning to align and translate, Bengio et. Hierarchical Attention Flow for Multiple-Choice Reading Comprehension Haichao Zhu, Furu Wei, Bing Qin, Ting Liu AAAI, 2018. 2015 and tries to give a step by step explanation of the (attention) model explained in their paper. Found inside – Page 177The reason is that although it introduces an attention mechanism, ... Based on this, this paper proposes a generative text summarization model based on the ...
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