Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures-Reference-Cited by-同舟云学术

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Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2023
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Author:

Bonab Hamed¹^ORCID,Joshi Ashutosh¹^ORCID,Bhatia Ravi²^ORCID,Gandhi Ankit²^ORCID,Huddar Vijay²^ORCID,Naik Juhi¹^ORCID,Al-Darabsah Mutasem¹^ORCID,Teo Choon Hui¹^ORCID,May Jonathan³^ORCID,Agarwal Tarun¹^ORCID,Petricek Vaclav¹^ORCID

Affiliation:

1. Amazon, USA

2. Amazon, India

3. Amazon, USA and USC Information Sciences Institute, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543873.3587629

Reference44 articles.

1. Retrieval Consistency in the Presence of Query Variations

2. Kuluhan Binici, Nam Trung Pham, Tulika Mitra, and Karianto Leman. 2021. Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data. CoRR abs/2108.05698 (2021). arXiv:2108.05698https://arxiv.org/abs/2108.05698

3. Leonid Boytsov and Eric Nyberg. 2020. Flexible retrieval with NMSLIB and FlexNeuART. arXiv preprint arXiv:2010.14848 (2020).

4. Model compression

5. Extreme Multi-label Learning for Semantic Matching in Product Search

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