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 Triple-to-Text Generation with an Anchor-to-Prototype Framework

2020 ◽  
Author(s):  
Ziran Li ◽  
Zibo Lin ◽  
Ning Ding ◽  
Hai-Tao Zheng ◽  
Ying Shen
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Experimental Results ◽  
Training Data ◽  
Generation Process ◽  
Text Generation ◽  
Language Generation ◽  
Structured Input ◽  
Textual Description ◽  
Specific Description

Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.

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 From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach

2017 ◽  
Author(s):  
Jiwei Tan ◽  
Xiaojun Wan ◽  
Jianguo Xiao
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Text Summarization ◽  
Experimental Results ◽  
Generation Task ◽  
Document Summarization ◽  
Real Dataset ◽  
Language Generation ◽  
Recent Success ◽  
Coarse To Fine

Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models to the document headline generation task. The challenge is that extending the sentence summarization model to consider more document information will mostly confuse the model and hurt the performance. In this paper, we propose a coarse-to-fine approach, which first identifies the important sentences of a document using document summarization techniques, and then exploits a multi-sentence summarization model with hierarchical attention to leverage the important sentences for headline generation. Experimental results on a large real dataset demonstrate the proposed approach significantly improves the performance of neural sentence summarization models on the headline generation task.

scholarly journals Multi-Agent Discussion Mechanism for Natural Language Generation

2019 ◽  
Vol 33 ◽  
pp. 6096-6103 ◽  
Author(s):  
Xu Li ◽  
Mingming Sun ◽  
Ping Li
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Text Summarization ◽  
Final Decision ◽  
Target Sequence ◽  
Text Generation ◽  
Time Step ◽  
Language Generation ◽  
Decoder Architecture ◽  
Multi Agent

We introduce the discussion mechanism into the multiagent communicating encoder-decoder architecture for Natural Language Generation (NLG) tasks and prove that by applying the discussion mechanism, the communication between agents becomes more effective. Generally speaking, an encoder-decoder architecture predicts target-sequence word by word in several time steps. At each time step of prediction, agents with the discussion mechanism predict the target word after several discussion steps. In the first step of discussion, agents make their choice independently and express their decision to other agents. In the next discussion step, agents collect other agents’ decision to update their own decisions, then express the updated decisions to others again. After several iterations, the agents make their final decision based on a well-communicated situation. The benefit of the discussion mechanism is that multiple encoders can be designed as different structures to fit the specified input or to fetch different representations of inputs.We train and evaluate the discussion mechanism on Table to Text Generation, Text Summarization and Image Caption tasks, respectively. Our empirical results demonstrate that the proposed multi-agent discussion mechanism is helpful for maximizing the utility of the communication between agents.

scholarly journals Natural Language Generation and Semantic Technologies

2014 ◽  
Vol 14 (2) ◽  
pp. 3-23 ◽  
Author(s):  
Kamenka Staykova
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Natural Language Generation ◽  
Semantic Technologies ◽  
Text Generation ◽  
Language Generation

Abstract The paper presents a survey of the domain of Natural Language Generation (NLG) with its models, techniques, applications, and investigates how the semantic technologies are drawn into text generation. The idea and facilities of Semantic Web initiative are discussed in connection with the new opportunities offered to the Natural Language Generation.

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 Real versus Template-Based Natural Language Generation: A False Opposition?

2005 ◽  
Vol 31 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Kees van Deemter ◽  
Mariët Theune ◽  
Emiel Krahmer
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Language Generation ◽  
Well Foundedness

This article challenges the received wisdom that template-based approaches to the generation of language are necessarily inferior to other approaches as regards their maintainability, linguistic well-foundedness, and quality of output. Some recent NLG systems that call themselves “template-based” will illustrate our claims.

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.

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