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Do People and Neural Nets Pay Attention to the Same Words
- Source :
- CIKM
- Publication Year :
- 2020
- Publisher :
- ACM, 2020.
-
Abstract
- We investigated how users evaluate passage-length answers for non-factoid questions. We conduct a study where answers were presented to users, sometimes shown with automatic word highlighting. Users were tasked with evaluating answer quality, correctness, completeness, and conciseness. Words in the answer were also annotated, both explicitly through user mark up and implicitly through user gaze data obtained from eye-tracking. Our results show that the correctness of an answer strongly depends on its completeness, conciseness is less important. Analysis of the annotated words showed correct and incorrect answers were assessed differently. Automatic highlighting helped users to evaluate answers quicker while maintaining accuracy, particularly when highlighting was similar to annotation. We fine-tuned a BERT model on a non-factoid QA task to examine if the model attends to words similar to those annotated. Similarity was found, consequently, we propose a method to exploit the BERT attention map to generate suggestions that simulate eye gaze during user evaluation.
- Subjects :
- Correctness
Computer science
business.industry
media_common.quotation_subject
Factoid
0211 other engineering and technologies
02 engineering and technology
010501 environmental sciences
computer.software_genre
01 natural sciences
Gaze
Task (project management)
Annotation
Completeness (order theory)
021105 building & construction
Eye tracking
Quality (business)
Artificial intelligence
business
computer
Natural language processing
0105 earth and related environmental sciences
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 29th ACM International Conference on Information & Knowledge Management
- Accession number :
- edsair.doi...........a9db930341db97b37631a475363f701a