@article{ghosh-etal-2023-pasta,
title = "{PASTA}: A Dataset for Modeling {PA}rticipant {STA}tes in Narratives",
author = "Ghosh, Sayontan and
Koupaee, Mahnaz and
Chen, Isabella and
Ferraro, Francis and
Chambers, Nathanael and
Balasubramanian, Niranjan",
journal = "Transactions of the Association for Computational Linguistics",
volume = "11",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2023.tacl-1.73",
doi = "10.1162/tacl_a_00600",
pages = "1283--1300",
abstract = "The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today{'}s LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g., physical, numerical, factual).1",
}
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<abstract>The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today’s LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g., physical, numerical, factual).1</abstract>
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%0 Journal Article
%T PASTA: A Dataset for Modeling PArticipant STAtes in Narratives
%A Ghosh, Sayontan
%A Koupaee, Mahnaz
%A Chen, Isabella
%A Ferraro, Francis
%A Chambers, Nathanael
%A Balasubramanian, Niranjan
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F ghosh-etal-2023-pasta
%X The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today’s LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g., physical, numerical, factual).1
%R 10.1162/tacl_a_00600
%U https://aclanthology.org/2023.tacl-1.73
%U https://doi.org/10.1162/tacl_a_00600
%P 1283-1300
Markdown (Informal)
[PASTA: A Dataset for Modeling PArticipant STAtes in Narratives](https://aclanthology.org/2023.tacl-1.73) (Ghosh et al., TACL 2023)
ACL