[PDF] Top 20 Simple Question Answering by Attentive Convolutional Neural Network
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Simple Question Answering by Attentive Convolutional Neural Network
... Since attentive matching of predicate-pattern is only one part of our jointly trained system, it is hard to judge whether or not an attentive CNN performs better than ...each question (converted into ... See full document
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Simple and Effective Semi Supervised Question Answering
... Pre-training: We make use of the generated cloze dataset to pre-train an expressive neural net- work designed for the task of reading comprehen- sion. We work with two publicly available neural ... See full document
6
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
... of simple QA over knowledge graphs, in our rush to explore ever sophisticated deep learning tech- niques, we have not adequately examined simple, strong baselines in a rigorous ...volve neural ... See full document
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Question Answering by Reasoning Across Documents with Graph Convolutional Networks
... In our approach, only a small query encoder, the GCN layers and a simple feed-forward an- swer selection component are learned. Instead of training RNN encoders, we use contextualized embeddings (ELMo) to obtain ... See full document
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Robust and Scalable Differentiable Neural Computer for Question Answering
... troduce the Stanford Attentive Reader which en- hances the attentive reader and adds a bilinear term to compute the attention between document and query. The Attention-Sum (AS) Reader from Kadlec et al. ... See full document
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BAG: Bi directional Attention Entity Graph Convolutional Network for Multi hop Reasoning Question Answering
... Question Answering (QA) and Machine Com- prehension (MC) tasks have drawn significant attention during the past ...deep neural mod- els, such as BiDAF (Seo et ...2017) answering a SQuAD ... See full document
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Simple Question Answering with Subgraph Ranking and Joint Scoring
... the question and the candidate facts in the subgraph and then find the best ...memory network to encode the questions and the facts to the same represen- tation space and score their ...an attentive ... See full document
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Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering
... incorporate them into the model. Some models such as (Yih et al., 2015; Dai et al., 2016) fo- cus on deep networks to encode question words and KB constituents. (Dai et al., 2016) have mod- eled the probability of ... See full document
5
Neural Domain Adaptation for Biomedical Question Answering
... Supervised Domain Adaptation In contrast to the unsupervised case, supervised domain adapta- tion assumes access to a small amount of labeled training data in the target domain. The simplest approach to supervised domain ... See full document
9
RankQA: Neural Question Answering with Answer Re Ranking
... in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text pas- sages are retrieved and, subsequently, a neural network for machine ... See full document
10
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
... Unlike SP-based approaches that usually assume a pre-defined set of lexical triggers or rules, which limit their domains and scalability, IR-based ap- proaches directly retrieve answers from the KB in light of the ... See full document
11
Training deep learning models to count based on synthetic data
... of answering the introduced research question, the first step is doing literature ...the question is ...learning, convolutional neural networks, counting problems, and synthetic ... See full document
50
A Neural Network for Factoid Question Answering over Paragraphs
... Recursive neural networks have achieved state-of-the-art performance in sentiment anal- ysis and parsing (Socher et al., 2013c; Hermann and Blunsom, 2013; Socher et al., 2013a). rnns have not been previously used ... See full document
12
Applying deep matching networks to Chinese medical question answering: a study and a dataset
... TextRank [15] algorithm to the re-ranking of candidates. His data were crawled from the web and not publicly available. The method was based on words and suffered from Chinese word segmentation failure in some cases. ... See full document
10
Question Answering over Freebase with Multi Column Convolutional Neural Networks
... using question-answer pairs, instead of annotated logical forms of ques- tions, as weak training signals (Liang et ...the question and these candidates to rank ...of question word embeddings to ... See full document
10
Learning to Compose Neural Networks for Question Answering
... geographical question-answering task first introduced by Krishnamurthy and Kollar ...sual question answering task much simpler than the one just discussed, and is appealing for a number of ... See full document
10
Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
... a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) ...region-based convolutional neural ... See full document
10
Deep Learning Based Crime Investigation Framework
... Deep Neural Network we can use LSTM model as shown in fig 4 for ...Recurrent Neural Networks [30] can remember the past states and makes use of the past information to make ... See full document
5
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Recently, deep learning has shown impressive results on both high-level and low- level vision problems. Face recognition has been one of the most active research areas in pattern recognition and computer vision for its ... See full document
8
Integrated Management System For Sugarcane Disease Using Deep Learning Techniques-A Review
... Artificial Neural Network such as Support Vector Machine and Convolution Neural Network, K- Means Clustering, Deep Learning, Image Processing, Neural Network and Binary Search ... See full document
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