Be Consistent! Improving Procedural Text Comprehension using Label

Be Consistent! Improving Procedural Text Comprehension using Label

Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Xinya Du, Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark, Claire Cardie

Allen Institute for Artificial Intelligence & Cornell University
Topic: Photosynthesis

Multiple
paragraphs

Prior SOTA predictions

Paragraph 1

CO2: moved, destroyed
Sugar: created

Paragraph 2

CO2: moved, destroyed
Sugar: created, moved

Paragraph 3

CO2: moved, destroyed
Sugar: created

Problem:
Inconsistency!

Proposed Learning Framework: Label Consistency Exploiter (LACE)
Novelty:
Adds consistency loss across paragraphs while training an end2end neural model.
Leverages unlabeled data to learn robust models when training data is limited.

Session: QA

Poster: 1889

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