Advances in Natural Language Generation

About Browse

Language generation (LG) is a crucial technology for human-machine communication. A good performance of language generation tasks implies (1) that machines are equipped with world knowledge that can require multimodal processing and reasoning (e.g. textual, visual and auditory inputs, or sensory data streams), and (2) the study of strong, novel machine learning (ML) methods (e.g. structured prediction, generative models), since virtually all state-of-the-art natural language processing (NLP) models are learned from data.

The aim of this collection is to bring together recent works related to developments and improvements within the field of language generation in a bid to advance scientific knowledge and encourage research into this field. The collection focuses on articles that are related but not limited to research concerning the following topics:

  • Multimodal language generation
  • Multilingual language generation
  • Multitask language generation
  • Creative language generation
  • Paraphrase generation
  • Translation of expressions, multiwords and phrasal units
  • Linguistic resources such as datasets of paraphrases
  • Association of expressions with identical or similar meaning
  • Effective ways to add value to the MT technology
  • Applications of language generation: machine translation, paraphrasing, text rewriting, cross-analysis of language varieties, summarisation, automatic feedback generation, text simplification, language teaching, etc.
  • Grounded multimodal reasoning and generation
  • Efficient machine learning algorithms, methods, and applications to language generation
  • Dialogue, interaction and conversational language generation applications
  • Large knowledge bases and graphs that can be used for language generation
  • Common sense reasoning in language generation
  • Applications of language generation in industry and society
  • ChatGPT: opportunities, challenges, and threats
  • Large language models and their generative power

The Collection has been developed by the Multi3Generation COST Action network (CA18231) but is open to submissions from all eligible researchers.

Collection advisors: Anabela Barreiro (Instituto de Engenharia de Sistemas e Computadores, Portugal), Elena Lloret Pastor, Max Silberztein