Papers by Luca Anselma
Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus (2024.lrec-main)
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Elisa Di Nuovo, Manuela Sanguinetti, Pier Felice Balestrucci, Luca Anselma, Cristian Bernareggi, Alessandro Mazzei
| Challenge: | a corpus of real-world dialogues between visually impaired users and an agent is described . the corpus is part of a larger research project aimed at developing tools for easier access to educational content for visually impaired people. |
| Approach: | They describe a corpus consisting of real-world dialogues between users and a task-oriented conversational agent . they report the results of a classification experiment on the annotated corpus and an additional experiment to assess the annotation capabilities of three large language models. |
| Outcome: | The proposed corpus is part of a larger research project aimed at improving visual aids for visually impaired users. |
Can Large Language Models Personalize Dialogues to Generational Styles? (2025.findings-emnlp)
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| Challenge: | a human evaluation reveals that annotators were able to most accurately identify the generation behind P-MultiWoZ dialogues, based only on a single query-reply pair. |
| Approach: | They create a personalized, generation-specific version of MultiWOZ 2.2 by prompting LLMs to generate personalized dialogue responses. |
| Outcome: | The proposed model is a personalized version of MultiWOZ 2.2 for Generation X, Y, and Z . it is validated by automatic and human evaluations to determine whether it reflects generational linguistic traits. |
Exploring Data Augmentation in Neural DRS-to-Text Generation (2024.eacl-long)
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| Challenge: | Neural networks are notoriously data-hungry, resulting in ungrammatical texts . data augmentation requires a specific design for a structurally rich input format . |
| Approach: | They propose to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |
| Outcome: | The proposed approach selectively augments a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |