# Query-oriented Data Augmentation for Session Search

arXiv: https://arxiv.org/abs/2407.03720 · HTML: https://mlsanity.com/paper/2407.03720/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [Optimizing Dense Retrieval Model Training with Hard Negatives](https://mlsanity.com/paper/2104.08051/) (2021)
- [End-to-End Neural Ad-hoc Ranking with Kernel Pooling](https://mlsanity.com/paper/1706.06613/) (2017)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Learning to Match Using Local and Distributed Representations of Text for Web Search](https://mlsanity.com/paper/1610.08136/) (2016)
- [Counterfactual Critic Multi-Agent Training for Scene Graph Generation](https://mlsanity.com/paper/1812.02347/) (2018)
- [Pytrec_eval: An Extremely Fast Python Interface to trec_eval](https://mlsanity.com/paper/1805.01597/) (2018)
- [SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval](https://mlsanity.com/paper/2210.11773/) (2022)
- [Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation](https://mlsanity.com/paper/2009.13815/) (2020)
- [Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking](https://mlsanity.com/paper/2108.10510/) (2021)
- [Context Attentive Document Ranking and Query Suggestion](https://mlsanity.com/paper/1906.02329/) (2019)
- [More Robust Dense Retrieval with Contrastive Dual Learning](https://mlsanity.com/paper/2107.07773/) (2021)
- [Context-Aware Attention-Based Data Augmentation for POI Recommendation](https://mlsanity.com/paper/2106.15984/) (2021)
- [Cross-language Sentence Selection via Data Augmentation and Rationale Training](https://mlsanity.com/paper/2106.02293/) (2021)
- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
- [Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search](https://mlsanity.com/paper/2208.10846/) (2022)

## Abstract (arXiv)

Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the relevance between search contexts and candidate documents. The common training paradigm is to pair the search context with different candidate documents and train the model to rank the clicked documents higher than the unclicked ones. However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i.e., the clicked documents can also be paired with different search contexts when training. In this work, we propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context, i.e., the current query, and train our model to rank the generated sequence along with the original sequence. This approach enables models to learn that the relevance of a document may vary as the session context changes, leading to a better understanding of users' search patterns. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty. Through experimentation on two extensive public search logs, we have successfully demonstrated the effectiveness of our model.