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    <abstract>We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies. Specifically, Relevant CI Parameters Extractor (RECIPE) seeks to answer questions posed by the theory of contextual integrity (CI) regarding the information flows described in the privacy statements. These questions have a simple syntactic structure and the answers are factoids or descriptive in nature. The model achieved an F1 score of 72.33, but we noticed that combining the results of this model with a neural dependency parser based approach yields a significantly higher F1 score of 92.35 compared to manual annotations. This indicates that future work which in-corporates signals from parsing like NLP tasks more explicitly can generalize better on out-of-domain tasks.</abstract>
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%0 Conference Proceedings
%T RECIPE: Applying Open Domain Question Answering to Privacy Policies
%A Shvartzshanider, Yan
%A Balashankar, Ananth
%A Wies, Thomas
%A Subramanian, Lakshminarayanan
%Y Choi, Eunsol
%Y Seo, Minjoon
%Y Chen, Danqi
%Y Jia, Robin
%Y Berant, Jonathan
%S Proceedings of the Workshop on Machine Reading for Question Answering
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F shvartzshanider-etal-2018-recipe
%X We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies. Specifically, Relevant CI Parameters Extractor (RECIPE) seeks to answer questions posed by the theory of contextual integrity (CI) regarding the information flows described in the privacy statements. These questions have a simple syntactic structure and the answers are factoids or descriptive in nature. The model achieved an F1 score of 72.33, but we noticed that combining the results of this model with a neural dependency parser based approach yields a significantly higher F1 score of 92.35 compared to manual annotations. This indicates that future work which in-corporates signals from parsing like NLP tasks more explicitly can generalize better on out-of-domain tasks.
%R 10.18653/v1/W18-2608
%U https://aclanthology.org/W18-2608
%U https://doi.org/10.18653/v1/W18-2608
%P 71-77

Markdown (Informal)

[RECIPE: Applying Open Domain Question Answering to Privacy Policies](https://aclanthology.org/W18-2608) (Shvartzshanider et al., ACL 2018)

• RECIPE: Applying Open Domain Question Answering to Privacy Policies (Shvartzshanider et al., ACL 2018)

ACL

• Yan Shvartzshanider, Ananth Balashankar, Thomas Wies, and Lakshminarayanan Subramanian. 2018. RECIPE: Applying Open Domain Question Answering to Privacy Policies. In Proceedings of the Workshop on Machine Reading for Question Answering, pages 71–77, Melbourne, Australia. Association for Computational Linguistics.