Agentività e telicità in GilBERTo: implicazioni cognitive
Authors: Agnese Lombardi, Alessandro Lenci (Università di Pisa, 2021)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics and use this information for the completion of morphosyntactic patterns. The semantic properties considered are telicity (also combined with definition) and agentivity. Both act at the interface between semantics and morphosyntax: they are semantically determined and synthetically encoded. The tasks were submitted to both the computational model and a group of Italian native speakers. The comparison between the two groups of data allows us to investigate to what extend neural language models capture significant aspects of human semantic competence. The aim of this study is to investigate whether neural language models based on Transformer infer semantic-lexical aspects relevant to the interface with syntax and use this information for the completion of morphosyntactic tasks. The semantic properties considered are telicity (also in relation to identification) and l'agentività. Both are semantically determined and syntactically coded. The tasks were submitted to both the model and the speakers. The comparison between the two groups of data will allow us to determine whether these computational models capture significant aspects of human semantic competence.

Introduction
The distributional hypothesis states that readings with similar linguistic contexts have a similar meaning (Wittgenstein, 1953; Harris, 1954; Firth, 1957). The distributional models have been successfully employed in many tasks of Natural Language Processing, but what are the knowledge acquired during the training process remains an open question. One of the approaches to understanding the nature of this language information is to evaluate their accuracy in psycholinguistic tasks. Some studies investigated syntactic properties and dependencies (Linzen et al., 2016; Ettinger, 2016; Wilcox et al., 2018; Futrell et al., 2019; Marvin and Linzen, 2018; Hu et al., 2020; Lau et al., 2020), others focused on semantic and pragmatic aspects such as: similarity (Hill et al., 2015), categorization (Baroni e Lenci, 2010), analogy (Mikolov et al., 2013), denial (Marvin and Linzen, 2018; Jumelet and Hupkes, 2018), pragmatic reasoning, semantic roles and eventive knowledge (Ettinger, 2020). Our work contributes to this line of research and adopts its psycholinguistic approach, but deviates from it in the properties investigated, proposing the analysis of telicity (in combination with identification) and of Agentivity. The objective is to investigate whether the inference of these semantic-lexical properties favours the elaboration of some morphosyntactic tasks. In our analysis we chose to use a predictive distributional model that uses contextualized representations (Peters et al., 2018; Devlin et al., 2019): GilBERTO, an Italian distributional model inspired by the architecture of RoBERTA (Liu et al., 2019).

Conclusion
The difference of elaboration between model and speakers allows us to propose implications from the theoretical point of view. The first implication is that although these models show a certain sensitivity and adherence to the way in which speakers process language, they cannot be considered a cognitive model of language processing. However, this analysis allows us to hypothesize the encoding of these properties of lexical semantics in the vector information of the distributional models, making light on which are the encoded semantic information. Surely there is a distributional influence in the way the speakers use the information, but it is also necessary to consider factors that depend on the extralinguistic context. In future works it makes sense to continue to investigate the elaboration of lexical semantics properties in distributional models, perhaps adopting other investigation techniques and comparing data extracted from different models. Future works will be able to investigate other properties of lexical semantics: e.g. split intransitivity. Moreover, our work can be improved by including the study of the verbal aspect and the influence it has in the interpretation of the sentence (conjugation of the sentences not only to the perfect aspect, but also to the imperfective aspect). For example, it might be interesting to consider the case of Slavic languages that grammarize telicity through the opposition between perfective and imperfective aspects. Finally, these studies could be used to implement these distributional models, improving the way in which they convey semantic compositionality (at a frasal level).