scholarly journals A Multi-Task Learning Framework for Abstractive Text Summarization

2019 ◽  
Vol 33 ◽  
pp. 9987-9988 ◽  
Author(s):  
Yao Lu ◽  
Linqing Liu ◽  
Zhile Jiang ◽  
Min Yang ◽  
Randy Goebel
Keyword(s):  
Text Categorization ◽  
Text Summarization ◽  
Significant Benefit ◽  
Experimental Results ◽  
Learning Approach ◽  
Learning Framework ◽  
Task Learning ◽  
Multiple Domains ◽  
Categorization Model ◽  
Three Components

We propose a Multi-task learning approach for Abstractive Text Summarization (MATS), motivated by the fact that humans have no difficulty performing such task because they have the capabilities of multiple domains. Specifically, MATS consists of three components: (i) a text categorization model that learns rich category-specific text representations using a bi-LSTM encoder; (ii) a syntax labeling model that learns to improve the syntax-aware LSTM decoder; and (iii) an abstractive text summarization model that shares its encoder and decoder with the text categorization and the syntax labeling tasks, respectively. In particular, the abstractive text summarization model enjoys significant benefit from the additional text categorization and syntax knowledge. Our experimental results show that MATS outperforms the competitors.1

scholarly journals A Multi-task Learning Approach for Image Captioning

2018 ◽  
Author(s):  
Wei Zhao ◽  
Benyou Wang ◽  
Jianbo Ye ◽  
Min Yang ◽  
Zhou Zhao ◽  
...  
Keyword(s):  
Object Classification ◽  
Classification Model ◽  
Learning Approach ◽  
Generation Model ◽  
Image Captioning ◽  
Generation Task ◽  
Task Learning ◽  
Image Representations ◽  
Multiple Domains ◽  
Image Descriptions

In this paper, we propose a Multi-task Learning Approach for Image Captioning (MLAIC ), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains. Specifically, MLAIC consists of three key components: (i) A multi-object classification model that learns rich category-aware image representations using a CNN image encoder; (ii) A syntax generation model that learns better syntax-aware LSTM based decoder; (iii) An image captioning model that generates image descriptions in text, sharing its CNN encoder and LSTM decoder with the object classification task and the syntax generation task, respectively. In particular, the image captioning model can benefit from the additional object categorization and syntax knowledge. To verify the effectiveness of our approach, we conduct extensive experiments on MS-COCO dataset. The experimental results demonstrate that our model achieves impressive results compared to other strong competitors.

scholarly journals A Multi-Task Learning Approach for Answer Selection: A Study and a Chinese Law Dataset

2019 ◽  
Vol 33 ◽  
pp. 9935-9936
Author(s):  
Wenyu Du ◽  
Baocheng Li ◽  
Min Yang ◽  
Qiang Qu ◽  
Ying Shen
Keyword(s):  
Question Answering ◽  
Selection Model ◽  
Classification Model ◽  
Learning Approach ◽  
Selection System ◽  
Document Representation ◽  
High Quality ◽  
Chinese Law ◽  
Task Learning ◽  
Multiple Domains

In this paper, we propose a Multi-Task learning approach for Answer Selection (MTAS), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains (tasks). Specifically, MTAS consists of two key components: (i) A category classification model that learns rich category-aware document representation; (ii) An answer selection model that provides the matching scores of question-answer pairs. These two tasks work on a shared document encoding layer, and they cooperate to learn a high-quality answer selection system. In addition, a multi-head attention mechanism is proposed to learn important information from different representation subspaces at different positions. We manually annotate the first Chinese question answering dataset in law domain (denoted as LawQA) to evaluate the effectiveness of our model. The experimental results show that our model MTAS consistently outperforms the compared methods.1

scholarly journals A Task Learning Mechanism for the Telerobots

2019 ◽  
Vol 16 (02) ◽  
pp. 1950009 ◽  
Author(s):  
Jing Luo ◽  
Chenguang Yang ◽  
Qiang Li ◽  
Min Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Complex Tasks ◽  
Learning Mechanism ◽  
Learning Framework ◽  
Task Learning

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot. Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method.

scholarly journals Hierarchical Multi-task Learning for Organization Evaluation of Argumentative Student Essays

2020 ◽  
Author(s):  
Wei Song ◽  
Ziyao Song ◽  
Lizhen Liu ◽  
Ruiji Fu
Keyword(s):  
Experimental Results ◽  
Learning Approach ◽  
Multiple Level ◽  
Automated Essay Scoring ◽  
Task Learning ◽  
Essay Scoring

Organization evaluation is an important dimension of automated essay scoring. This paper focuses on discourse element (i.e., functions of sentences and paragraphs) based organization evaluation. Existing approaches mostly separate discourse element identification and organization evaluation. In contrast, we propose a neural hierarchical multi-task learning approach for jointly optimizing sentence and paragraph level discourse element identification and organization evaluation. We represent the organization as a grid to simulate the visual layout of an essay and integrate discourse elements at multiple linguistic levels. Experimental results show that the multi-task learning based organization evaluation can achieve significant improvements compared with existing work and pipeline baselines. Multiple level discourse element identification also benefits from multi-task learning through mutual enhancement.

scholarly journals A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13817-13818
Author(s):  
Minni Jain ◽  
Maitree Leekha ◽  
Mononito Goswami
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Experimental Results ◽  
Single Domain ◽  
Learning Approach ◽  
Open Domain ◽  
Consumer Reviews ◽  
Multiple Domains

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.

scholarly journals Correction to: Arabic text summarization using deep learning approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Molham Al-Maleh ◽  
Said Desouki
Keyword(s):  
Deep Learning ◽  
Text Summarization ◽  
Learning Approach ◽  
Arabic Text ◽  
Arabic Text Summarization

An amendment to this paper has been published and can be accessed via the original article.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

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