@inproceedings{nangia-bowman-2018-listops,
title = "{L}ist{O}ps: A Diagnostic Dataset for Latent Tree Learning",
author = "Nangia, Nikita and
Bowman, Samuel",
editor = "Cordeiro, Silvio Ricardo and
Oraby, Shereen and
Pavalanathan, Umashanthi and
Rim, Kyeongmin",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-4013",
doi = "10.18653/v1/N18-4013",
pages = "92--99",
abstract = "Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.",
}
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<abstract>Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.</abstract>
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%0 Conference Proceedings
%T ListOps: A Diagnostic Dataset for Latent Tree Learning
%A Nangia, Nikita
%A Bowman, Samuel
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F nangia-bowman-2018-listops
%X Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.
%R 10.18653/v1/N18-4013
%U https://aclanthology.org/N18-4013
%U https://doi.org/10.18653/v1/N18-4013
%P 92-99
Markdown (Informal)
[ListOps: A Diagnostic Dataset for Latent Tree Learning](https://aclanthology.org/N18-4013) (Nangia & Bowman, NAACL 2018)
ACL
- Nikita Nangia and Samuel Bowman. 2018. ListOps: A Diagnostic Dataset for Latent Tree Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 92–99, New Orleans, Louisiana, USA. Association for Computational Linguistics.