M3. Integrating quantum theory in NLP tasks and applications
Once we have determined both the best way to represent the information contained in a text using quantum theory (M1) and the way to integrate quantum algorithms for NLP (M2), this module, related to OB4, will analyse the possible scenarios for the application of these findings. To do that, it is essential to first analyse how to model several linguistic phenomena, with special attention to areas where quantum advancements could provide significant improvements, achieving more accurate and precise results compared to current approaches.
Three main tasks will be addressed, one of them related to text understanding and the other two to text
production.
Task 3.1. Exploration of quantum NLP approaches to resolve linguistic phenomena.
In order to analyse how quantum theory can help to improve NLP tasks related to text understanding and generation, it is necessary to analyse those aspects related to these tasks where current approaches are not able to provide robust results. Therefore, phenomena such as anaphora resolution, word sense disambiguation or metaphor resolution will be investigated.
Milestone: Proposal with a novel approach that treats complex linguistic phenomena using quantum
techniques.
Task 3.2. Exploration of quantum NLP approaches to perform text summaries.
This task aims to formalise the summarisation problem using principles from quantum theory, offering a novel perspective on how summarisation can be viewed and optimised as an information selection and generation problem. From the knowledge acquired in modules 1 and 2, and task 3.1, quantum circuits will be used to represent and model the summarisation process, potentially leading to breakthroughs in efficiency and accuracy. Then, quantum-inspired optimisation processes would be used to conduct summarisation modeling for fitness evaluation, taking as a basis the ideas already investigated using classical optimisation algorithms (Zamuda and Lloret, 2020; Zamuda, Dugonik and Lloret, 2024) as well as the advances and novel ideas for using quantum extracted from the literature (Niroula et al., 2022; Ulker and Ozer, 2024).
Milestone: Analysis and proposal of a novel quantum-inspired summarisation approach.
Task 3.3. Exploration of quantum NLP approaches to perform automatic text simplification.
Following the same guidelines as for the previous task (task 3.2), this task aims to analyse how to optimise the automatic text simplification process using the principle of quantum theory. Although several studies have been carried out so far on the automatic simplification of texts, not all the aspects to be simplified have been solved to the same extent. While linguistic obstacles such as numbers, superlatives, acronyms, enumerations or simple appositions have sufficiently robust solutions (see as an example SIMPLE.TEXT tool in https://simpletext.demos.gplsi.es/), other types of obstacles such as some cases of difficult words or complex sentences still require some effort on the part of the scientific community so that the results obtained can really be of use to society (Saggion, 2024; Martínez, et al., 2024). Therefore, in this task, novel perspectives based on quantum algorithms will be studied to obtain substantial improvements in the automatic text simplification processes. This will be based on studies carried out so far on the use of such algorithms in other complex NLP tasks such as machine translation (Varmantchaonala, et al. 2024; Abbaszade, et al. 2023) or summary generation (Piwowarski, Amini and Llamas, 2012).
Milestone: Novel approaches for the analysis and generation of summaries and simplified texts based on quantum theory.