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        <title>Effects of Naturalistic Variation in Goal-Oriented Dialog</title>
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        <namePart type="given">Jatin</namePart>
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    <name type="personal">
        <namePart type="given">Kahini</namePart>
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    <abstract>Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are constructed through crowdsourcing, where the crowd workers follow a fixed template of instructions while enacting the role of a user/agent. This results in straight-forward, somewhat routine, and mostly trouble-free conversations, as crowd workers do not think to represent the full range of actions that occur naturally with real users. In this work, we investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new and more effective testbeds for both datasets, by introducing naturalistic variation by the user. We observe that there is a significant drop in performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet and GLMP on both datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.358</identifier>
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%0 Conference Proceedings
%T Effects of Naturalistic Variation in Goal-Oriented Dialog
%A Ganhotra, Jatin
%A Moore, Robert
%A Joshi, Sachindra
%A Wadhawan, Kahini
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ganhotra-etal-2020-effects
%X Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are constructed through crowdsourcing, where the crowd workers follow a fixed template of instructions while enacting the role of a user/agent. This results in straight-forward, somewhat routine, and mostly trouble-free conversations, as crowd workers do not think to represent the full range of actions that occur naturally with real users. In this work, we investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new and more effective testbeds for both datasets, by introducing naturalistic variation by the user. We observe that there is a significant drop in performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet and GLMP on both datasets.
%R 10.18653/v1/2020.findings-emnlp.358
%U https://aclanthology.org/2020.findings-emnlp.358/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.358
%P 4013-4020

Markdown (Informal)

[Effects of Naturalistic Variation in Goal-Oriented Dialog](https://aclanthology.org/2020.findings-emnlp.358/) (Ganhotra et al., Findings 2020)

• Effects of Naturalistic Variation in Goal-Oriented Dialog (Ganhotra et al., Findings 2020)

ACL

• Jatin Ganhotra, Robert Moore, Sachindra Joshi, and Kahini Wadhawan. 2020. Effects of Naturalistic Variation in Goal-Oriented Dialog. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4013–4020, Online. Association for Computational Linguistics.