Natural Language Generation and Machine Learning
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 5752
Special Issue Editor
Special Issue Information
Dear Colleagues,
The generation of natural language has always been one of the core tasks in the area of natural language processing, with a very wide spectrum of applications ranging from summarization systems to conversational agents. However, it was only quite recently that it received rekindled interest from the research community, mostly due to vast improvement of the linguistic capacity of neural generators and the wide adoption of more sophisticated generation systems within commercial personal assistants.
Natural language generation is an umbrella term for a variety of tasks that are usually categorized according to the different given input (e.g., text, unstructured/semi-structured/ structured meaning representations, images, dialogue history) and output formats (e.g., sentence, document, caption, dialogue utterance) they deal with. The most common tasks include text summarization, data-to-text generation with the input ranging from semi-structured record–field–value tables to meaning representations and RDF triples, caption generation of images, and conversational response generation in the context of either a closed- or open-domain dialogue. Given the subjectivity of the task and, often, the lack of large enough datasets with multiple reference text outputs, evaluating the quality of the output becomes a significant bottleneck in the deployment of a successful generation system.
The goal of this Special Issue is to present a collection of the current research in data-driven approaches to natural language generation using machine learning, aiming to capture a variety of tasks, modeling approaches, and effective evaluation techniques. We are interested in submissions of high-quality, original, technical and survey papers addressing both theoretical and practical aspects. We wish for this Special Issue to not only showcase systems with state-of-the-art performance on a specific task, but also studies that consider the ethical implications and the potential impact on society of such systems with regards to generating output which is of high fidelity, factual, and does not contradict common sense knowledge.
Dr. Ioannis Konstas
Guest Editor
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Keywords
- natural language generation
- natural language processing
- data-to-text generation
- text summarization
- knowledge base generation
- conversational response generation
- multilingual generation
- caption generation
- generation for low-resource languages
- quality estimation for NLG
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