@inproceedings{parrish-etal-2021-putting-linguist,
title = "Does Putting a Linguist in the Loop Improve {NLU} Data Collection?",
author = "Parrish, Alicia and
Huang, William and
Agha, Omar and
Lee, Soo-Hwan and
Nangia, Nikita and
Warstadt, Alexia and
Aggarwal, Karmanya and
Allaway, Emily and
Linzen, Tal and
Bowman, Samuel R.",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.421/",
doi = "10.18653/v1/2021.findings-emnlp.421",
pages = "4886--4901",
abstract = "Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work {\textquoteleft}in the loop' during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection."
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<abstract>Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work ‘in the loop’ during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.</abstract>
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%0 Conference Proceedings
%T Does Putting a Linguist in the Loop Improve NLU Data Collection?
%A Parrish, Alicia
%A Huang, William
%A Agha, Omar
%A Lee, Soo-Hwan
%A Nangia, Nikita
%A Warstadt, Alexia
%A Aggarwal, Karmanya
%A Allaway, Emily
%A Linzen, Tal
%A Bowman, Samuel R.
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F parrish-etal-2021-putting-linguist
%X Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work ‘in the loop’ during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.
%R 10.18653/v1/2021.findings-emnlp.421
%U https://aclanthology.org/2021.findings-emnlp.421/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.421
%P 4886-4901
Markdown (Informal)
[Does Putting a Linguist in the Loop Improve NLU Data Collection?](https://aclanthology.org/2021.findings-emnlp.421/) (Parrish et al., Findings 2021)
ACL
- Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, and Samuel R. Bowman. 2021. Does Putting a Linguist in the Loop Improve NLU Data Collection?. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4886–4901, Punta Cana, Dominican Republic. Association for Computational Linguistics.