Details of Grant 

EPSRC Reference:	EP/T02450X/1
Title:	Modeling Idiomaticity in Human and Artificial Language Processing
Principal Investigator:	Villavicencio, Professor A
Other Investigators:	Scarton, Dr C Korhonen, Professor A
Researcher Co-Investigators:
Project Partners:	Aix-Marseille University
Department:	Computer Science
Organisation:	University of Sheffield
Scheme:	Standard Research
Starts:	01 December 2020	Ends:	30 November 2023	Value (£):	446,163
EPSRC Research Topic Classifications:	Artificial Intelligence
EPSRC Industrial Sector Classifications:	Information Technologies
Related Grants:
Panel History:	Panel Date Panel Name Outcome 10 Feb 2020 Responsible NLP for Intelligent Interfaces Panel 2020 Announced
Summary on Grant Application Form
This project will develop computational models with the ability to recognize and accurately process idiomatic (non-literal) language that are linguistically motivated and cognitively-inspired by human processing data. Equipping models with the ability to process idiomatic expressions is particularly important for obtaining more accurate representations as these can lead to gains in downstream tasks, such as machine translation and text simplification. The originality of this work is in integrating linguistic and cognitive clues about human idiomatic language processing in the construction of models for word and phrase representations, and in integrating them in downstream tasks. The main objectives and research challenges of this project: 1:To explore cognitive and linguistic clues linked to idiomaticity that can be used to guide models for word and phrase representations 2: To investigate idiomatically-aware models. 3: To explore alternative forms of integrating these models in applications and to develop a framework for idiomaticity evaluation in word and phrase representation models. 4. To release software implementations of the proposed models to facilitate reproducibility and wider adoption by the research community. This proposal targets a crucial limitation in standard NLP models, as idiomaticity is everywhere in human communication, with potential benefits to various applications that include natural language interfaces, such as conversational agents, computer assisted language learning platforms, question answering and information retrieval systems. As a consequence we anticipate the proposal will have a wide academic impact in the community. Moreover, enabling more precise language understanding and generation also has the potential of enhancing accessibility and digital inclusion, through promoting more natural and accurate communication between humans and machines. We intend to demonstrate the additional potential benefits of these models through interactions with our external collaborations, including by means of an advisory board. The board will include other academics and industrial partners working on related topics, such as, Dr. Fabio Kepler (Unbabel Portugal, for machine translation), Prof. Mathieu Constant (Université de Lorraine, France, for parsing and idiomaticity), Prof. Lucia Specia (Imperial College, UK, for text simplification) and Dr. Afsaneh Fazly (Samsung, Canada, for idiomaticity). This proposal targets a crucial limitation in standard NLP models, as idiomaticity is everywhere in human communication, with potential benefits to various applications that include natural language interfaces, such as conversational agents, computer assisted language learning platforms, question answering and information retrieval systems. As a consequence we anticipate the proposal will have a wide academic impact in the community. Moreover, enabling more precise language understanding and generation also has the potential of enhancing accessibility and digital inclusion, through promoting more natural and accurate communication between humans and machines. We intend to demonstrate the additional potential benefits of these models through interactions with our external collaborations, including by means of an advisory board.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Project URL:
Further Information:
Organisation Website:	http://www.shef.ac.uk