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DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce

Ipsita Mohanty


Abstract
Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting.
Anthology ID:
2022.ecnlp-1.1
Volume:
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Shervin Malmasi, Oleg Rokhlenko, Nicola Ueffing, Ido Guy, Eugene Agichtein, Surya Kallumadi
Venue:
ECNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/2022.ecnlp-1.1
DOI:
10.18653/v1/2022.ecnlp-1.1
Bibkey:
Cite (ACL):
Ipsita Mohanty. 2022. DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 1–7, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce (Mohanty, ECNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ecnlp-1.1.pdf
Video:
 https://aclanthology.org/2022.ecnlp-1.1.mp4