The Lab vs The Crowd: An Investigation into Data Quality for Neural
Dialogue Models
- URL: http://arxiv.org/abs/2012.03855v1
- Date: Mon, 7 Dec 2020 17:02:00 GMT
- Title: The Lab vs The Crowd: An Investigation into Data Quality for Neural
Dialogue Models
- Authors: Jos\'e Lopes, Francisco J. Chiyah Garcia and Helen Hastie
- Abstract summary: We compare the performance of dialogue models for the same interaction task but collected in two settings: in the lab vs. crowd-sourced.
We find that fewer lab dialogues are needed to reach similar accuracy, less than half the amount of lab data as crowd-sourced data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Challenges around collecting and processing quality data have hampered
progress in data-driven dialogue models. Previous approaches are moving away
from costly, resource-intensive lab settings, where collection is slow but
where the data is deemed of high quality. The advent of crowd-sourcing
platforms, such as Amazon Mechanical Turk, has provided researchers with an
alternative cost-effective and rapid way to collect data. However, the
collection of fluid, natural spoken or textual interaction can be challenging,
particularly between two crowd-sourced workers. In this study, we compare the
performance of dialogue models for the same interaction task but collected in
two different settings: in the lab vs. crowd-sourced. We find that fewer lab
dialogues are needed to reach similar accuracy, less than half the amount of
lab data as crowd-sourced data. We discuss the advantages and disadvantages of
each data collection method.
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