@inproceedings{liu-etal-2022-towards,
title = "Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs",
author = "Liu, Zheng and
Zhang, Wei and
Chen, Yan and
Sun, Weiyi and
Du, Tianchuan and
Schroeder, Benjamin",
editor = "Malmasi, Shervin and
Rokhlenko, Oleg and
Ueffing, Nicola and
Guy, Ido and
Agichtein, Eugene and
Kallumadi, Surya",
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-1.26",
doi = "10.18653/v1/2022.ecnlp-1.26",
pages = "224--233",
abstract = "Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not really help generalization which aligns with the discovery of prior art, yet proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a manually annotated query-product relevance data.",
}
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%0 Conference Proceedings
%T Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
%A Liu, Zheng
%A Zhang, Wei
%A Chen, Yan
%A Sun, Weiyi
%A Du, Tianchuan
%A Schroeder, Benjamin
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F liu-etal-2022-towards
%X Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not really help generalization which aligns with the discovery of prior art, yet proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a manually annotated query-product relevance data.
%R 10.18653/v1/2022.ecnlp-1.26
%U https://aclanthology.org/2022.ecnlp-1.26
%U https://doi.org/10.18653/v1/2022.ecnlp-1.26
%P 224-233
Markdown (Informal)
[Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs](https://aclanthology.org/2022.ecnlp-1.26) (Liu et al., ECNLP 2022)
ACL