Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive

• URL: http://arxiv.org/abs/2307.02910v1
• Date: Thu, 6 Jul 2023 10:52:22 GMT
• Title: Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive
• Authors: Agnese Lombardi, Alessandro Lenci
• Abstract summary: The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
• Score: 77.71680953280436
• License: http://creativecommons.org/licenses/by/4.0/
• 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 definiteness) and agentivity. Both act at the interface between semantics and morphosyntax: they are semantically determined and syntactically 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 extent neural language models capture significant aspects of human semantic competence.

Related papers

• Contextual modulation of language comprehension in a dynamic neural model of lexical meaning [0.0]
  We demonstrate the architecture and behavior of the model using as a test case the English lexical item 'have', focusing on its polysemous use. Results support a novel perspective on lexical polysemy: that the many related meanings of a word are metastable neural activation states.
  arXiv  Detail & Related papers  (2024-07-19T23:28:55Z)
• Identifying and interpreting non-aligned human conceptual representations using language modeling [0.0]
  We show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains. We find that blind individuals more strongly associate social and cognitive meanings to verbs related to motion. For some verbs, representations of blind and sighted are highly similar.
  arXiv  Detail & Related papers  (2024-03-10T13:02:27Z)
• Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity [68.8204255655161]
  We present the semantic notion of agentivity as a case study for probing such interactions. This suggests LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery.
  arXiv  Detail & Related papers  (2023-05-29T16:24:01Z)
• Lexical semantics enhanced neural word embeddings [4.040491121427623]
  hierarchy-fitting is a novel approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies. Results demonstrate the efficacy of hierarchy-fitting in specialising neural embeddings with semantic relations in late fusion.
  arXiv  Detail & Related papers  (2022-10-03T08:10:23Z)
• Learning Disentangled Representations for Natural Language Definitions [0.0]
  We argue that recurrent syntactic and semantic regularities in textual data can be used to provide the models with both structural biases and generative factors. We leverage the semantic structures present in a representative and semantically dense category of sentence types, definitional sentences, for training a Variational Autoencoder to learn disentangled representations.
  arXiv  Detail & Related papers  (2022-09-22T14:31:55Z)
• Quantifying Synthesis and Fusion and their Impact on Machine Translation [79.61874492642691]
  In Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level. For computing literature, we test unsupervised and supervised morphological segmentation methods for English, German and Turkish, whereas for fusion, we propose a semi-automatic method using Spanish as a case study. Then, we analyse the relationship between machine translation quality and the degree of synthesis and fusion at word (nouns and verbs for English-Turkish,
  arXiv  Detail & Related papers  (2022-05-06T17:04:58Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• Schr\"odinger's Tree -- On Syntax and Neural Language Models [10.296219074343785]
  Language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities. We observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form. We outline the implications of the different types of research questions exhibited in studies on syntax.
  arXiv  Detail & Related papers  (2021-10-17T18:25:23Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.