Luca Anselma


2024

pdf bib
Exploring Data Augmentation in Neural DRS-to-Text Generation
Muhammad Saad Amin | Luca Anselma | Alessandro Mazzei
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural networks are notoriously data-hungry. This represents an issue in cases where data are scarce such as in low-resource languages. Data augmentation is a technique commonly used in computer vision to provide neural networks with more data and increase their generalization power. When dealing with data augmentation for natural language, however, simple data augmentation techniques similar to the ones used in computer vision such as rotation and cropping cannot be employed because they would generate ungrammatical texts. Thus, data augmentation needs a specific design in the case of neural logic-to-text systems, especially for a structurally rich input format such as the ones used for meaning representation. This is the case of the neural natural language generation for Discourse Representation Structures (DRS-to-Text), where the logical nature of DRS needs a specific design of data augmentation. In this paper, we adopt a novel approach in DRS-to-Text to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. In particular, we propose using WordNet supersenses to produce new training sentences using both in-and-out-of-context nouns. We present a number of experiments for evaluating the role played by augmented lexical information. The experimental results prove the effectiveness of our approach for data augmentation in DRS-to-Text generation.

pdf bib
Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus
Elisa Di Nuovo | Manuela Sanguinetti | Pier Felice Balestrucci | Luca Anselma | Cristian Bernareggi | Alessandro Mazzei
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper describes a corpus consisting of real-world dialogues in English between users and a task-oriented conversational agent, with interactions revolving around the description of finite state automata. The creation of this corpus is part of a larger research project aimed at developing tools for an easier access to educational content, especially in STEM fields, for users with visual impairments. The development of this corpus was precisely motivated by the aim of providing a useful resource to support the design of such tools. The core feature of this corpus is that its creation involved both sighted and visually impaired participants, thus allowing for a greater diversity of perspectives and giving the opportunity to identify possible differences in the way the two groups of participants interacted with the agent. The paper introduces this corpus, giving an account of the process that led to its creation, i.e. the methodology followed to obtain the data, the annotation scheme adopted, and the analysis of the results. Finally, the paper reports the results of a classification experiment on the annotated corpus, and an additional experiment to assess the annotation capabilities of three large language models, in view of a further expansion of the corpus.

2020

pdf bib
Content Selection for Explanation Requests in Customer-Care Domain
Luca Anselma | Mirko Di Lascio | Dario Mana | Alessandro Mazzei | Manuela Sanguinetti
2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

This paper describes a content selection module for the generation of explanations in a dialogue system designed for customer care domain. First we describe the construction of a corpus of a dialogues containing explanation requests from customers to a virtual agent of a telco, and second we study and formalize the importance of a specific information content for the generated message. In particular, we adapt the notions of importance and relevance in the case of schematic knowledge bases.

2018

pdf bib
Designing and testing the messages produced by a virtual dietitian
Luca Anselma | Alessandro Mazzei
Proceedings of the 11th International Conference on Natural Language Generation

This paper presents a project about the automatic generation of persuasive messages in the context of the diet management. In the first part of the paper we introduce the basic mechanisms related to data interpretation and content selection for a numerical data-to-text generation architecture. In the second part of the paper we discuss a number of factors influencing the design of the messages. In particular, we consider the design of the aggregation procedure. Finally, we present the results of a human-based evaluation concerning this design factor.

pdf bib
CheckYourMeal!: diet management with NLG
Luca Anselma | Simone Donetti | Alessandro Mazzei | Andrea Pirone
Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)