@inproceedings{yuan-etal-2020-interactive-machine,
title = "Interactive Machine Comprehension with Information Seeking Agents",
author = "Yuan, Xingdi and
Fu, Jie and
C{\^o}t{\'e}, Marc-Alexandre and
Tay, Yi and
Pal, Chris and
Trischler, Adam",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.211/",
doi = "10.18653/v1/2020.acl-main.211",
pages = "2325--2338",
abstract = "Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we {\textquotedblleft}occlude{\textquotedblright} the majority of a document`s text and add context-sensitive commands that reveal {\textquotedblleft}glimpses{\textquotedblright} of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios."
}
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<abstract>Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we “occlude” the majority of a document‘s text and add context-sensitive commands that reveal “glimpses” of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios.</abstract>
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%0 Conference Proceedings
%T Interactive Machine Comprehension with Information Seeking Agents
%A Yuan, Xingdi
%A Fu, Jie
%A Côté, Marc-Alexandre
%A Tay, Yi
%A Pal, Chris
%A Trischler, Adam
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yuan-etal-2020-interactive-machine
%X Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we “occlude” the majority of a document‘s text and add context-sensitive commands that reveal “glimpses” of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios.
%R 10.18653/v1/2020.acl-main.211
%U https://aclanthology.org/2020.acl-main.211/
%U https://doi.org/10.18653/v1/2020.acl-main.211
%P 2325-2338
Markdown (Informal)
[Interactive Machine Comprehension with Information Seeking Agents](https://aclanthology.org/2020.acl-main.211/) (Yuan et al., ACL 2020)
ACL