Sampling Approach Matters: Active Learning for Robotic Language Acquisition

TL;DR

This paper investigates how different active learning strategies impact data efficiency in robotic language acquisition, emphasizing the importance of representativeness and diversity in sample selection across various grounded language tasks.

Contribution

It introduces a method to analyze data complexity in joint language learning problems and evaluates how task characteristics and design choices influence active learning effectiveness.

Findings

Representativeness and diversity are key in sample selection.

Task complexity and model choices significantly affect learning efficiency.

Active learning improves data efficiency in grounded language tasks.

Abstract

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.