Attention-based CNN Matching Net
نویسندگان
چکیده
In this paper, we introduce attention-based CNN matching net (ACM-Net), an end-to-end neural network for question answering. ACM-Net matches between the given passage, query and multiple answer choices, and then it extracts features from passage and choices based on query information. We also propose a two-staged CNN architecture and a query-based attention mechanism in our model. These two component can effectively find out the most important parts in passage according to the query. Finally, we extract features from those important parts and find out the most possible answer choice. We conduct this model on the MovieQA dataset [1] using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
منابع مشابه
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) mo...
متن کاملDiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used in NLP tasks to capture the longterm and local dependencies respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time, and flexibility in modeling dependencies. We propose a novel attention mechanism in which the atte...
متن کاملPN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
In this paper we propose a new approach for learning local descriptors for matching image patches. It has recently been demonstrated that descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Unfortunately their computational complexity is prohibitive for any practical application. We address this problem and propose a CNN based descriptor ...
متن کاملLearning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images
The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model...
متن کاملEdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching
Recently convolutional neural network (CNN) promotes the development of stereo matching greatly. Especially those end-to-end stereo methods achieve best performance. However less attention is paid on encoding context information, simplifying two-stage disparity learning pipeline and improving details in disparity maps. Differently we focus on these problems. Firstly, we propose an one-stage con...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1709.05036 شماره
صفحات -
تاریخ انتشار 2017