CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Jiayuan Mao Xuelin Yang Xikun Zhang Noah D. Goodman Jiajun Wu
Building machines that can reason about physical events and their causal
relationships is crucial for flexible interaction with the physical world.
However, most existing physical and causal reasoning benchmarks are exclusively
based on synthetically generated events and synthetic natural language
descriptions of causal relationships. This design brings up two issues. First,
there is a lack of diversity in both event types and natural language
descriptions; second, causal relationships based on manually-defined heuristics
are different from human judgments. To address both shortcomings, we present
the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of
physical events with human labels. We employ two techniques to improve data
collection efficiency: first, a novel iterative event cloze task to elicit a
new representation of events in videos, which we term Causal Event Graphs
(CEGs); second, a data augmentation technique based on neural language
generative models. We convert the collected CEGs into questions and answers to
be consistent with prior work. Finally, we study a collection of baseline
approaches for CLEVRER-Humans question-answering, highlighting the great
challenges set forth by our benchmark.