Interactive Language Learning by Question Answering

08/28/2019
by   Xingdi Yuan, et al.
0

Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.

READ FULL TEXT
research
02/28/2018

Medical Exam Question Answering with Large-scale Reading Comprehension

Reading and understanding text is one important component in computer ai...
research
08/27/2019

Interactive Machine Comprehension with Information Seeking Agents

Existing machine reading comprehension (MRC) models do not scale effecti...
research
08/31/2021

Interactive Machine Comprehension with Dynamic Knowledge Graphs

Interactive machine reading comprehension (iMRC) is machine comprehensio...
research
12/09/2017

IQA: Visual Question Answering in Interactive Environments

We introduce Interactive Question Answering (IQA), the task of answering...
research
11/11/2019

Meta Answering for Machine Reading

We investigate a framework for machine reading, inspired by real world i...
research
05/19/2023

Graphologue: Exploring Large Language Model Responses with Interactive Diagrams

Large language models (LLMs) have recently soared in popularity due to t...
research
01/14/2021

TSQA: Tabular Scenario Based Question Answering

Scenario-based question answering (SQA) has attracted an increasing rese...

Please sign up or login with your details

Forgot password? Click here to reset