@inproceedings{masis-anderson-2021-prosper,
title = "{P}ro{SP}er: Probing Human and Neural Network Language Model Understanding of Spatial Perspective",
author = "Masis, Tessa and
Anderson, Carolyn",
editor = "Bastings, Jasmijn and
Belinkov, Yonatan and
Dupoux, Emmanuel and
Giulianelli, Mario and
Hupkes, Dieuwke and
Pinter, Yuval and
Sajjad, Hassan",
booktitle = "Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.blackboxnlp-1.8",
doi = "10.18653/v1/2021.blackboxnlp-1.8",
pages = "95--135",
abstract = "Understanding perspectival language is important for applications like dialogue systems and human-robot interaction. We propose a probe task that explores how well language models understand spatial perspective. We present a dataset for evaluating perspective inference in English, ProSPer, and use it to explore how humans and Transformer-based language models infer perspective. Although the best bidirectional model performs similarly to humans, they display different strengths: humans outperform neural networks in conversational contexts, while RoBERTa excels at written genres.",
}
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<abstract>Understanding perspectival language is important for applications like dialogue systems and human-robot interaction. We propose a probe task that explores how well language models understand spatial perspective. We present a dataset for evaluating perspective inference in English, ProSPer, and use it to explore how humans and Transformer-based language models infer perspective. Although the best bidirectional model performs similarly to humans, they display different strengths: humans outperform neural networks in conversational contexts, while RoBERTa excels at written genres.</abstract>
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%0 Conference Proceedings
%T ProSPer: Probing Human and Neural Network Language Model Understanding of Spatial Perspective
%A Masis, Tessa
%A Anderson, Carolyn
%Y Bastings, Jasmijn
%Y Belinkov, Yonatan
%Y Dupoux, Emmanuel
%Y Giulianelli, Mario
%Y Hupkes, Dieuwke
%Y Pinter, Yuval
%Y Sajjad, Hassan
%S Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F masis-anderson-2021-prosper
%X Understanding perspectival language is important for applications like dialogue systems and human-robot interaction. We propose a probe task that explores how well language models understand spatial perspective. We present a dataset for evaluating perspective inference in English, ProSPer, and use it to explore how humans and Transformer-based language models infer perspective. Although the best bidirectional model performs similarly to humans, they display different strengths: humans outperform neural networks in conversational contexts, while RoBERTa excels at written genres.
%R 10.18653/v1/2021.blackboxnlp-1.8
%U https://aclanthology.org/2021.blackboxnlp-1.8
%U https://doi.org/10.18653/v1/2021.blackboxnlp-1.8
%P 95-135
Markdown (Informal)
[ProSPer: Probing Human and Neural Network Language Model Understanding of Spatial Perspective](https://aclanthology.org/2021.blackboxnlp-1.8) (Masis & Anderson, BlackboxNLP 2021)
ACL