Consistent and explainable natural language inference

• Grant number: 23/00694-4
• Support Opportunities: Regular Research Grants
• Start date: May 01, 2023
• End date: October 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Agreement: University of Manchester
• Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Daniel Carlos Guimarães Pedronette
• Principal researcher abroad: Andre do Nascimento Freitas
• Institution abroad: University of Manchester, England
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

Natural Language Processing (NLP) refers to a branch of artificial intelligence (AI) concerned with giving computers the ability of interpreting and reasoning over human languages. Intrinsically multidisciplinary, it involves integrates diverse research areas, such as linguistics, logics, computer science, and artificial intelligence. In a world mainly guided by technology and increasingly supported by AI-based tools, the NLP field plays a fundamental hole, supporting technologies for machine translation (MT), question-answering (QA), information retrieval, text generation, and recommendation systems. Pressed by the advances of deep learning and data-driven approaches in other fields, such as computer vision and pattern recognition, NLP field have experienced the advent and popularity of black-box techniques. While such approaches are often very effective, they are often less interpretable. In this scenario, increasing interest in explanations can be observed and a growing understanding of the importance of explainability. This research project aims to investigate the construction of explanatory chains for natural language inference based on contextual rank analysis and consistency. Such research direction represents an intersection of research interests between groups of State University of São Paulo (UNESP) and the University of Manchester. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LETICIO, GUSTAVO ROSSETO; VALEM, LUCAS PASCOTTI; LOPES, LEONARDO TADEU; GUIMARAES PEDRONETTE, DANIEL CARLOS. pyUDLF: A Python Framework for Unsupervised Distance Learning Tasks. PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023, v. N/A, p. 5-pg., 2023-01-01. (23/00694-4, 18/15597-6)

KAWAI, VINICIUS SATO; VALEM, LUCAS PASCOTTI; BALDASSIN, ALEXANDRO; BORIN, EDSON; PEDRONETTE, DANIEL CARLOS GUIMARAES; LATECKI, LONGIN JAN. Rank-based Hashing for Effective and Efficient Nearest Neighbor Search for Image Retrieval. ACM Transactions on Multimedia Computing Communications and Applications, v. 20, n. 10, p. 19-pg., 2024-10-01. (18/15519-5, 23/00694-4, 18/15597-6)