scholarly journals Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge

2020 ◽  
Vol 59 ◽  
pp. 123-156 ◽  
Author(s):  
Ondřej Dušek ◽  
Jekaterina Novikova ◽  
Verena Rieser
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
The State ◽  
Language Generation ◽  
End To End

scholarly journals Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

2018 ◽  
Vol 61 ◽  
pp. 65-170 ◽  
Author(s):  
Albert Gatt ◽  
Emiel Krahmer
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Data Driven ◽  
Research Topics ◽  
Language Generation ◽  
The Past ◽  
Current State ◽  
Linguistic Input ◽  
New Applications

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of NLP, with an emphasis on different evaluation methods and the relationships between them.

scholarly journals SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks

2018 ◽  
Author(s):  
Ke Wang ◽  
Xiaojun Wan
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Poor Quality ◽  
Experimental Results ◽  
Text Generation ◽  
Language Generation ◽  
Adversarial Networks

Generating texts of different sentiment labels is getting more and more attention in the area of natural language generation. Recently, Generative Adversarial Net (GAN) has shown promising results in text generation. However, the texts generated by GAN usually suffer from the problems of poor quality, lack of diversity and mode collapse. In this paper, we propose a novel framework - SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems. In our framework, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision. We propose a penalty based objective in the generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own examples of a specific sentiment label accurately. Experimental results on four datasets demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the sentiment accuracy and quality of generated texts.

scholarly journals Natural language generation: The commercial state of the art in 2020

2020 ◽  
Vol 26 (4) ◽  
pp. 481-487
Author(s):  
Robert Dale
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Commercial Software ◽  
Language Generation ◽  
The Future ◽  
Mainstream Press

AbstractIt took a while, but natural language generation is now an established commercial software category. It’s commented upon frequently in both industry media and the mainstream press, and businesses are willing to pay hard cash to take advantage of the technology. We look at who’s active in the space, the nature of the technology that’s available today and where things might go in the future.

scholarly journals Hierarchical Attention Networks for Sentence Ordering

2019 ◽  
Vol 33 ◽  
pp. 7184-7191
Author(s):  
Tianming Wang ◽  
Xiaojun Wan
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Attention Network ◽  
Attention Networks ◽  
Language Generation ◽  
Visualization Analysis ◽  
Discourse Coherence ◽  
Model Visualization

Modeling discourse coherence is an important problem in natural language generation and understanding. Sentence ordering, the goal of which is to organize a set of sentences into a coherent text, is a commonly used task to learn and evaluate the model. In this paper, we propose a novel hierarchical attention network that captures word clues and dependencies between sentences to address this problem. Our model outperforms prior methods and achieves state-of-the-art performance on several datasets in different domains. Furthermore, our experiments demonstrate that the model performs very well even though adding noisy sentences into the set, which shows the robustness and effectiveness of the model. Visualization analysis and case study show that our model captures the structure and pattern of coherent texts not only by simple word clues but also by consecution in context.

scholarly journals MOSS: End-to-End Dialog System Framework with Modular Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 8327-8335
Author(s):  
Weixin Liang ◽  
Youzhi Tian ◽  
Chengcai Chen ◽  
Zhou Yu
Keyword(s):  
Natural Language ◽  
Customer Service ◽  
State Of The Art ◽  
Training Data ◽  
Language Understanding ◽  
Language Generation ◽  
Dialog System ◽  
Major Bottleneck ◽  
End To End ◽  
Task Oriented

A major bottleneck in training end-to-end task-oriented dialog system is the lack of data. To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning and natural language generation. With only 60% of the training data, MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms state-of-the-art models on CamRest676. Moreover, introducing modular supervision has even bigger benefits when the dialog task has a more complex dialog state and action space. With only 40% of the training data, MOSS-all outperforms the state-of-the-art model on a complex laptop network trouble shooting dataset, LaptopNetwork, that we introduced. LaptopNetwork consists of conversations between real customers and customer service agents in Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision from different dialog modules at both framework level and model level. Therefore, MOSS is extremely flexible to update in real-world deployment.

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