Keyphrase Generation with Word Attention

2019 ◽  
pp. 270-281
Author(s):  
Hai Huang ◽  
Tianshuo Huang ◽  
Longxuan Ma ◽  
Lei Zhang
Keyword(s):  
Word Attention

scholarly journals Perceptions and Attitudes towards Mules in a Group of Sodiers

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1009
Author(s):  
Javiera Lagos ◽  
Manuel Rojas ◽  
Joao B. Rodrigues ◽  
Tamara Tadich
Keyword(s):  
Text Mining ◽  
Pain Perception ◽  
Health Needs ◽  
Emotional Needs ◽  
Mountainous Areas ◽  
Working Capacity ◽  
Perceptions And Attitudes ◽  
Wide Range ◽  
Positive Correlations ◽  
Word Attention

Mules are essential for pack work in mountainous areas, but there is a lack of research on this species. This study intends to assess the perceptions, attitudes, empathy and pain perception of soldiers about mules, to understand the type of human–mule relationship. For this, a survey was applied with closed-ended questions where the empathy and pain perception tools were included and later analyzed through correlations. Open-ended questions were analyzed through text mining. A total of 73 soldiers were surveyed. They had a wide range of ages and years of experience working with equids. Significant positive correlations were found between human empathy, animal empathy and pain perception. Soldiers show a preference for working with mules over donkeys and horses. Text mining analysis shows three clusters associated with the mules’ nutritional, environmental and health needs. In the same line, relevant relations were found for the word “attention” with “load”, “food”, and “harness”. When asked what mules signify for them, two clusters were found, associated with mules’ working capacity and their role in the army. Relevant relations were found between the terms “mountain”, “support”, and “logistics”, and also between “intelligent” and “noble”. To secure mules’ behavioral and emotional needs, future training strategies should include behavior and welfare concepts.

scholarly journals Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

2020 ◽  
Vol 34 (05) ◽  
pp. 8504-8511
Author(s):  
Arindam Mitra ◽  
Ishan Shrivastava ◽  
Chitta Baral
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
New Learning ◽  
Word Attention ◽  
Attention Function

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.

PTWA: Pre-Training with Word Attention for Chinese Named Entity Recognition

2021 ◽  
Author(s):  
Kaixin Ma ◽  
Meiling Liu ◽  
Tiejun Zhao ◽  
Jiyun Zhou ◽  
Yang Yu
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Word Attention

Incorporating word attention with convolutional neural networks for abstractive summarization

2019 ◽  
Vol 23 (1) ◽  
pp. 267-287
Author(s):  
Chengzhe Yuan ◽  
Zhifeng Bao ◽  
Mark Sanderson ◽  
Yong Tang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Attention ◽  
Abstractive Summarization

Sub-word attention mechanism and ensemble learning-based semantic annotation for heterogeneous networks

2020 ◽  
Vol 18 (1) ◽  
pp. 51
Author(s):  
Xu Zhang ◽  
Gang Liu ◽  
Yuanfeng Yang ◽  
Zhaobin Liu ◽  
Jinxiang Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Heterogeneous Networks ◽  
Semantic Annotation ◽  
Attention Mechanism ◽  
Word Attention

scholarly journals Supervised attention for answer selection in community question answering

2020 ◽  
Vol 9 (2) ◽  
pp. 203
Author(s):  
Thanh Thi Ha ◽  
Atsuhiro Takasu ◽  
Thanh Chinh Nguyen ◽  
Kiem Hieu Nguyen ◽  
Van Nha Nguyen ◽  
...  
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Irrelevant Information ◽  
Social Question ◽  
Community Question Answering ◽  
Basic Model ◽  
Proposed Model ◽  
Questions And Answers ◽  
Word Attention ◽  
Better Than

<span class="fontstyle0">Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores </span><span class="fontstyle2">matchLSTM </span><span class="fontstyle0">for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into </span><span class="fontstyle2">matchLSTM</span><span class="fontstyle0">. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.</span> <br /><br />

Sub-word attention mechanism and ensemble learning-based semantic annotation for heterogeneous networks

2020 ◽  
Vol 18 (1) ◽  
pp. 51
Author(s):  
Liang Zhang ◽  
Zhaobin Liu ◽  
Jinxiang Li ◽  
Gang Liu ◽  
Yuanfeng Yang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Heterogeneous Networks ◽  
Semantic Annotation ◽  
Attention Mechanism ◽  
Word Attention

scholarly journals Articulatory-to-Acoustic Conversion Using BiLSTM-CNN Word-Attention-Based Method

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guofeng Ren ◽  
Guicheng Shao ◽  
Jianmei Fu
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Graphical Representation ◽  
Rapid Development ◽  
Gaussian Mixture ◽  
Semantic Features ◽  
Electromagnetic Articulography ◽  
Articulatory Features ◽  
Word Attention

In the recent years, along with the development of artificial intelligence (AI) and man-machine interaction technology, speech recognition and production have been asked to adapt to the rapid development of AI and man-machine technology, which need to improve recognition accuracy through adding novel features, fusing the feature, and improving recognition methods. Aiming at developing novel recognition feature and application to speech recognition, this paper presents a new method for articulatory-to-acoustic conversion. In the study, we have converted articulatory features (i.e., velocities of tongue and motion of lips) into acoustic features (i.e., the second formant and Mel-Cepstra). By considering the graphical representation of the articulators’ motion, this study combined Bidirectional Long Short-Term Memory (BiLSTM) with convolution neural network (CNN) and adopted the idea of word attention in Mandarin to extract semantic features. In this paper, we used the electromagnetic articulography (EMA) database designed by Taiyuan University of Technology, which contains ten speakers’ 299 disyllables and sentences of Mandarin, and extracted 8-dimensional articulatory features and 1-dimensional semantic feature relying on the word-attention layer; we then trained 200 samples and tested 99 samples for the articulatory-to-acoustic conversion. Finally, Root Mean Square Error (RMSE), Mean Mel-Cepstral Distortion (MMCD), and correlation coefficient have been used to evaluate the conversion effect and for comparison with Gaussian Mixture Model (GMM) and BiLSTM of recurrent neural network (BiLSTM-RNN). The results illustrated that the MMCD of Mel-Frequency Cepstrum Coefficient (MFCC) was 1.467 dB, and the RMSE of F2 was 22.10 Hz. The research results of this study can be used in the features fusion and speech recognition to improve the accuracy of recognition.

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