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        <title>Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation</title>
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    <name type="personal">
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    <abstract>In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e.g. The editor finished the article), which are typically interpreted by inferring a hidden event (e.g. reading) on the basis of contextual cues. This paper tackles the problem of logical metonymy interpretation, that is, the retrieval of the covert event via computational methods. We compare different types of models, including the probabilistic and the distributional ones previously introduced in the literature on the topic. For the first time, we also tested on this task some of the recent Transformer-based models, such as BERT, RoBERTa, XLNet, and GPT-2. Our results show a complex scenario, in which the best Transformer-based models and some traditional distributional models perform very similarly. However, the low performance on some of the testing datasets suggests that logical metonymy is still a challenging phenomenon for computational modeling.</abstract>
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%0 Conference Proceedings
%T Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation
%A Rambelli, Giulia
%A Chersoni, Emmanuele
%A Lenci, Alessandro
%A Blache, Philippe
%A Huang, Chu-Ren
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Suzhou, China
%F rambelli-etal-2020-comparing
%X In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e.g. The editor finished the article), which are typically interpreted by inferring a hidden event (e.g. reading) on the basis of contextual cues. This paper tackles the problem of logical metonymy interpretation, that is, the retrieval of the covert event via computational methods. We compare different types of models, including the probabilistic and the distributional ones previously introduced in the literature on the topic. For the first time, we also tested on this task some of the recent Transformer-based models, such as BERT, RoBERTa, XLNet, and GPT-2. Our results show a complex scenario, in which the best Transformer-based models and some traditional distributional models perform very similarly. However, the low performance on some of the testing datasets suggests that logical metonymy is still a challenging phenomenon for computational modeling.
%U https://aclanthology.org/2020.aacl-main.26
%P 224-234

Markdown (Informal)

[Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation](https://aclanthology.org/2020.aacl-main.26) (Rambelli et al., AACL 2020)

• Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation (Rambelli et al., AACL 2020)

ACL

• Giulia Rambelli, Emmanuele Chersoni, Alessandro Lenci, Philippe Blache, and Chu-Ren Huang. 2020. Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 224–234, Suzhou, China. Association for Computational Linguistics.