# Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce

arXiv: https://arxiv.org/abs/2301.00036 · HTML: https://mlsanity.com/paper/2301.00036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution](https://mlsanity.com/paper/1611.04051/) (2016)
- [Query expansion techniques for information retrieval: A survey](https://mlsanity.com/paper/1708.00247/) (2017)
- [Simplified Data Wrangling with ir_datasets](https://mlsanity.com/paper/2103.02280/) (2021)
- [Query Expansion with Locally-Trained Word Embeddings](https://mlsanity.com/paper/1605.07891/) (2016)
- [An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain](https://mlsanity.com/paper/1902.00592/) (2019)
- [ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine](https://mlsanity.com/paper/2010.10789/) (2020)
- [WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset](https://mlsanity.com/paper/1912.01901/) (2019)
- [A new approach for query expansion using Wikipedia and WordNet](https://mlsanity.com/paper/1901.10197/) (2019)
- [Exploring Query Categorisation for Query Expansion: A Study](https://mlsanity.com/paper/1509.05567/) (2015)
- [GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback](https://mlsanity.com/paper/2108.06010/) (2021)

## Built on by (most cited first, 1 of 1)

- [Alibaba International E-commerce Product Search Competition DILAB Team Technical Report](https://mlsanity.com/paper/2510.18499/) (2025)

## Abstract (arXiv)

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models