# Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques

arXiv: https://arxiv.org/abs/2208.10751 · HTML: https://mlsanity.com/paper/2208.10751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Rethinking embedding coupling in pre-trained language models](https://mlsanity.com/paper/2010.12821/) (2020)
- [Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search](https://mlsanity.com/paper/2206.06588/) (2022)
- [Multi-Sample Dropout for Accelerated Training and Better Generalization](https://mlsanity.com/paper/1905.09788/) (2019)

## Abstract (arXiv)

We present an effective way to predict search query-item relationship. We combine pre-trained transformer and LSTM models, and increase model robustness using adversarial training, exponential moving average, multi-sampled dropout, and diversity based ensemble, to tackle an extremely difficult problem of predicting against queries not seen before. All of our strategies focus on increasing robustness of deep learning models and are applicable in any task where deep learning models are used. Applying our strategies, we achieved 10th place in KDD Cup 2022 Product Substitution Classification task.