Interactive Machine Comprehension with Information Seeking Agents

By Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Tay, C. Pal, Adam Trischler. 2019

19 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Question Answering & Reading Comprehension

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Citations in the corpus

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022, background): “Several papers have explored the use of the web for information extraction [34] and retrieval [1], question answering [57, 25], dialog [45], and training language models on webtext [2].”
Quark: Controllable Text Generation with Reinforced Unlearning (2022, background): “Recent works have explored applying RL on tasks such as question-answering [84, 85, 47, 83, 84], summarization [58, 53, 70, 60, 17, 51], and machine translation [58, 87, 79, 82, 81, 13, 66, 5, 49].”
Boosting Search Engines with Interactive Agents (2021, neutral): “From a similar angle, Yuan et al. (2020) oﬀer promising ﬁndings on training QA agents with RL for template-based information gathering and answering actions.”
What Question Answering can Learn from Trivia Nerds (2019, background): “”, you can ignore the rest of the question and just type Ctrl+F (Yuan et al., 2019; Russell, 2020) to find the third country—Australia in this case—that appears with “Canada and the UK”.”