@inproceedings{mohanty-2022-deftri,
title = "{DEFT}ri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce",
author = "Mohanty, Ipsita",
booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ecnlp-1.1",
doi = "10.18653/v1/2022.ecnlp-1.1",
pages = "1--7",
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.",
}
Markdown (Informal)
[DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce](https://aclanthology.org/2022.ecnlp-1.1) (Mohanty, ECNLP 2022)
ACL