scholarly journals Chinese Medical Question Answer Matching based on Interactive Sentence Representation Learning

2020 ◽  
Author(s):  
Xiongtao Cui ◽  
Jungang Han
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Representation Learning ◽  
Semantic Features ◽  
Learning Method ◽  
Open Domain ◽  
Semantic Association ◽  
Performance Deficits ◽  
Multi Scale ◽  
Questions And Answers

Chinese medical question-answer matching is more challenging than the open-domain questionanswer matching in English. Even though the deep learning method has performed well in improving the performance of question-answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.

scholarly journals Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tiantian Chen ◽  
Nianbin Wang ◽  
Hongbin Wang ◽  
Haomin Zhan
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Features ◽  
Distant Supervision ◽  
Word Level ◽  
Proposed Model ◽  
Relation Prediction ◽  
Better Than

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

scholarly journals Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

2021 ◽  
Author(s):  
Ming Jin ◽  
Yizhen Zheng ◽  
Yuan-Fang Li ◽  
Chen Gong ◽  
Chuan Zhou ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Vital Role ◽  
Graph Representation ◽  
Input Graph ◽  
Global Perspectives ◽  
Multi Scale ◽  
Recent Success ◽  
Real World Datasets ◽  
Siamese Networks

Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Specifically, we first generate two augmented views from the input graph based on local and global perspectives. Then, we employ two objectives called cross-view and cross-network contrastiveness to maximize the agreement between node representations across different views and networks. To demonstrate the effectiveness of our approach, we perform empirical experiments on five real-world datasets. Our method not only achieves new state-of-the-art results but also surpasses some semi-supervised counterparts by large margins. Code is made available at https://github.com/GRAND-Lab/MERIT

scholarly journals MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

2021 ◽  
Vol 9 ◽  
pp. 1389-1406
Author(s):  
Shayne Longpre ◽  
Yi Lu ◽  
Joachim Daiber
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Linguistically Diverse ◽  
Data Representation ◽  
Independent Data ◽  
Open Domain ◽  
Low Resource ◽  
Art Methods ◽  
Questions And Answers ◽  
Cross Lingual

Abstract Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1

scholarly journals Progressive Open-Domain Response Generation with Multiple Controllable Attributes

2021 ◽  
Author(s):  
Haiqin Yang ◽  
Xiaoyuan Yao ◽  
Yiqun Duan ◽  
Jianping Shen ◽  
Jie Zhong ◽  
...  
Keyword(s):  
Latent Variables ◽  
State Of The Art ◽  
Semantic Features ◽  
Dialogue Systems ◽  
Open Domain ◽  
Specific Variable ◽  
Variational Autoencoder ◽  
Attribute Information ◽  
Global Variable ◽  
The Common

It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.

scholarly journals Towards Gene Function Prediction via Multi-Networks Representation Learning

2019 ◽  
Vol 33 ◽  
pp. 10069-10070 ◽  
Author(s):  
Hansheng Xue ◽  
Jiajie Peng ◽  
Xuequn Shang
Keyword(s):  
Gene Function ◽  
State Of The Art ◽  
Representation Learning ◽  
Information Loss ◽  
Function Prediction ◽  
Experimental Results ◽  
Learning Method ◽  
Gene Function Prediction ◽  
Networks Integration ◽  
Information Loss Problem

Multi-networks integration methods have achieved prominent performance on many network-based tasks, but these approaches often incur information loss problem. In this paper, we propose a novel multi-networks representation learning method based on semi-supervised autoencoder, termed as DeepMNE, which captures complex topological structures of each network and takes the correlation among multinetworks into account. The experimental results on two realworld datasets indicate that DeepMNE outperforms the existing state-of-the-art algorithms.

scholarly journals Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

2021 ◽  
Vol 13 (6) ◽  
pp. 1049
Author(s):  
Cheng Liao ◽  
Han Hu ◽  
Haifeng Li ◽  
Xuming Ge ◽  
Min Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Building Extraction ◽  
Joint Learning ◽  
Multi Scale ◽  
New Strategy ◽  
Contour Information

Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

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