scholarly journals Attentive User-Engaged Adversarial Neural Network for Community Question Answering

2020 ◽  
Vol 34 (05) ◽  
pp. 9322-9329
Author(s):  
Yuexiang Xie ◽  
Ying Shen ◽  
Yaliang Li ◽  
Min Yang ◽  
Kai Lei
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Question Answering ◽  
Selection Process ◽  
Coarse Grained ◽  
User Engagement ◽  
Fine Grained ◽  
Community Question Answering ◽  
Questions And Answers ◽  
Real World Datasets

We study the community question answering (CQA) problem that emerges with the advent of numerous community forums in the recent past. The task of finding appropriate answers to questions from informative but noisy crowdsourced answers is important yet challenging in practice. We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. A novel attentive mechanism is incorporated to model the semantic internal and external relations among questions, answers and user contexts. To handle the noise issue caused by introducing user context, we design a two-step denoise mechanism, including a coarse-grained selection process by similarity measurement, and a fine-grained selection process by applying an adversarial training module. We evaluate the proposed method on large-scale real-world datasets SemEval-2016 and SemEval-2017. Experimental results verify the benefits of incorporating user information, and show that our proposed model significantly outperforms the state-of-the-art methods.

scholarly journals PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices

2020 ◽  
Vol 34 (04) ◽  
pp. 5117-5124 ◽  
Author(s):  
Xiaolong Ma ◽  
Fu-Ming Guo ◽  
Wei Niu ◽  
Xue Lin ◽  
Jian Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Mobile Devices ◽  
Large Scale ◽  
Coarse Grained ◽  
Convolution Kernel ◽  
Fine Grained ◽  
Model Compression ◽  
Weight Pruning ◽  
Balanced Workload

Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight pruning is a straightforward and effective method. There are currently two mainstreams of pruning methods representing two extremes of pruning regularity: non-structured, fine-grained pruning can achieve high sparsity and accuracy, but is not hardware friendly; structured, coarse-grained pruning exploits hardware-efficient structures in pruning, but suffers from accuracy drop when the pruning rate is high. In this paper, we introduce PCONV, comprising a new sparsity dimension, – fine-grained pruning patterns inside the coarse-grained structures. PCONV comprises two types of sparsities, Sparse Convolution Patterns (SCP) which is generated from intra-convolution kernel pruning and connectivity sparsity generated from inter-convolution kernel pruning. Essentially, SCP enhances accuracy due to its special vision properties, and connectivity sparsity increases pruning rate while maintaining balanced workload on filter computation. To deploy PCONV, we develop a novel compiler-assisted DNN inference framework and execute PCONV models in real-time without accuracy compromise, which cannot be achieved in prior work. Our experimental results show that, PCONV outperforms three state-of-art end-to-end DNN frameworks, TensorFlow-Lite, TVM, and Alibaba Mobile Neural Network with speedup up to 39.2 ×, 11.4 ×, and 6.3 ×, respectively, with no accuracy loss. Mobile devices can achieve real-time inference on large-scale DNNs.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2868
Author(s):  
Wenxuan Zhao ◽  
Yaqin Zhao ◽  
Liqi Feng ◽  
Jiaxi Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Large Scale ◽  
Learning Strategy ◽  
Contextual Information ◽  
Small Scale ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

scholarly journals Relevance-guided Supervision for OpenQA with ColBERT

2021 ◽  
Vol 9 ◽  
pp. 929-944
Author(s):  
Omar Khattab ◽  
Christopher Potts ◽  
Matei Zaharia
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Training Data ◽  
Coarse Grained ◽  
Retrieval Model ◽  
Open Domain ◽  
Weak Supervision ◽  
Fine Grained ◽  
Vector Representations ◽  
Large Corpus

Abstract Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages. We argue that this modeling choice is insufficiently expressive for dealing with the complexity of natural language questions. To address this, we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT to OpenQA. ColBERT creates fine-grained interactions between questions and passages. We propose an efficient weak supervision strategy that iteratively uses ColBERT to create its own training data. This greatly improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system attains state-of-the-art extractive OpenQA performance on all three datasets.

Answer Selection in Community Question Answering Using LSTM

2021 ◽  
pp. 153-165
Author(s):  
Saman Qureshi ◽  
Sri Khetwat Saritha ◽  
D. Kishan
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Attention Mechanism ◽  
Generalization Ability ◽  
Mechanism Model ◽  
Community Question Answering ◽  
Informative Part ◽  
External Sources

Websites like Quora, Yahoo! Answers, and Reddit are examples of community question answering (CQA) systems that enable users to ask questions as well as to answer questions. Answer selection is the most challenging task in CQA systems to get the good and relevant answer for the user questions. The shortcomings in the current approaches are lexical gap between text pairs, dependency on external sources, and manual features which lead to lack of generalization ability. These shortcomings are resolved by already proposed work, but they lack generalization, and their performance is not satisfying. Whereas to focus on rich quality answers, attention mechanism can be integrates with neural network. This chapter proposes two models BLSTM and BLSTM with attention mechanism. Attention mechanism aligns question to the answer with the answer's more informative part. So, when it is applied in the model, BLSTM with attention mechanism model surpasses the top approaches.

scholarly journals Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1001 ◽  
Author(s):  
Jingang Liu ◽  
Chunhe Xia ◽  
Haihua Yan ◽  
Wenjing Xu
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Training Model ◽  
Entity Recognition ◽  
Coarse Grained ◽  
Neural Network Modeling ◽  
Fine Grained ◽  
Named Entity

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks.

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