In this section
@article{Lertvittayakumjorn:2021:10.1162/tacl_a_00440,
author = {Lertvittayakumjorn, P and Toni, F},
doi = {10.1162/tacl_a_00440},
journal = {Transactions of the Association for Computational Linguistics},
pages = {1508--1528},
title = {Explanation-based human debugging of nlp models: a survey},
url = {http://dx.doi.org/10.1162/tacl_a_00440},
volume = {9},
year = {2021}
}
TY - JOUR
AB - Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.
AU - Lertvittayakumjorn,P
AU - Toni,F
DO - 10.1162/tacl_a_00440
EP - 1528
PY - 2021///
SN - 2307-387X
SP - 1508
TI - Explanation-based human debugging of nlp models: a survey
T2 - Transactions of the Association for Computational Linguistics
UR - http://dx.doi.org/10.1162/tacl_a_00440
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000751952200090&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00440/108932/Explanation-Based-Human-Debugging-of-NLP-Models-A
UR - http://hdl.handle.net/10044/1/104462
VL - 9
ER -
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