scholarly journals An Iterative Polishing Framework Based on Quality Aware Masked Language Model for Chinese Poetry Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7643-7650
Author(s):  
Liming Deng ◽  
Jie Wang ◽  
Hangming Liang ◽  
Hui Chen ◽  
Zhiqiang Xie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Model ◽  
Automatic Generation ◽  
Context Information ◽  
Chinese Poetry ◽  
Model Structure ◽  
Automatic Evaluation ◽  
Highly Qualified ◽  
Polishing Process ◽  
Global Context

Owing to its unique literal and aesthetical characteristics, automatic generation of Chinese poetry is still challenging in Artificial Intelligence, which can hardly be straightforwardly realized by end-to-end methods. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. In the first stage, an encoder-decoder structure is utilized to generate a poem draft. Afterwards, our proposed Quality-Aware Masked Language Model (QA-MLM) is employed to polish the draft towards higher quality in terms of linguistics and literalness. Based on a multi-task learning scheme, QA-MLM is able to determine whether polishing is needed based on the poem draft. Furthermore, QA-MLM is able to localize improper characters of the poem draft and substitute with newly predicted ones accordingly. Benefited from the masked language model structure, QA-MLM incorporates global context information into the polishing process, which can obtain more appropriate polishing results than the unidirectional sequential decoding. Moreover, the iterative polishing process will be terminated automatically when QA-MLM regards the processed poem as a qualified one. Both human and automatic evaluation have been conducted, and the results demonstrate that our approach is effective to improve the performance of encoder-decoder structure.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

Automatic Story Generation

2021 ◽  
Vol 54 (5) ◽  
pp. 1-38
Author(s):  
Arwa I. Alhussain ◽  
Aqil M. Azmi
Keyword(s):  
Artificial Intelligence ◽  
Automatic Generation ◽  
Evaluation Methods ◽  
Extensive Study ◽  
Computational Creativity ◽  
Story Generation ◽  
To Receive ◽  
Shed Light

Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.

scholarly journals Breakdown Detection in Negotiation Dialogues (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13969-13970
Author(s):  
Atsuki Yamaguchi ◽  
Katsuhide Fujita
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Language Model ◽  
Experimental Results ◽  
Conflicts Of Interests ◽  
Early Stages ◽  
End To End ◽  
Gated Recurrent Unit

In human-human negotiation, reaching a rational agreement can be difficult, and unfortunately, the negotiations sometimes break down because of conflicts of interests. If artificial intelligence can play a role in assisting with human-human negotiation, it can assist in avoiding negotiation breakdown, leading to a rational agreement. Therefore, this study focuses on end-to-end tasks for predicting the outcome of a negotiation dialogue in natural language. Our task is modeled using a gated recurrent unit and a pre-trained language model: BERT as the baseline. Experimental results demonstrate that the proposed tasks are feasible on two negotiation dialogue datasets, and that signs of a breakdown can be detected in the early stages using the baselines even if the models are used in a partial dialogue history.

scholarly journals Artificial intelligence and copyright

2021 ◽  
pp. 15-22
Author(s):  
Daryna Prylypko
Keyword(s):  
Artificial Intelligence ◽  
Computer Program ◽  
Key Words ◽  
Automatic Generation ◽  
The Other ◽  
International Level ◽  
Human Agent ◽  
Foreign Countries ◽  
The One ◽  
Justice Practice

Key words: copyright, work, artificial intelligence, computer program In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.

scholarly journals Artificial Intelligence – Challenges and Chances for Europe

2020 ◽  
pp. 1-17
Author(s):  
Joseph Straus
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
Social Issues ◽  
Building Blocks ◽  
Economic Environment ◽  
Public Attention ◽  
Global Context ◽  
Fourth Industrial Revolution ◽  
The Eu

As one of the building blocks of the fourth industrial revolution, artificial intelligence has attracted much public attention and sparked protracted discussions about its impact on future technological, economic and social developments. This contribution conveys insights into artificial intelligence’s basic methods and tools, its main achievements, its economic environment and the surrounding ethical and social issues. Based on the announced and taken measures of the EU organs in the area of artificial intelligence, the contribution analyses the position of Europe in the global context.

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