# Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

arXiv: https://arxiv.org/abs/1708.03418 · HTML: https://mlsanity.com/paper/1708.03418/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 22 of 22)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://mlsanity.com/paper/2203.03367/) (2022)
- [From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational System](https://mlsanity.com/paper/2508.15811/) (2025)
- [Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding](https://mlsanity.com/paper/2305.14449/) (2023)
- [Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes](https://mlsanity.com/paper/2307.15455/) (2023)
- [Capability-aware Prompt Reformulation Learning for Text-to-Image Generation](https://mlsanity.com/paper/2403.19716/) (2024)
- [Deep Natural Language Processing for LinkedIn Search](https://mlsanity.com/paper/2108.13300/) (2021)
- [Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites](https://mlsanity.com/paper/2209.07584/) (2022)
- [PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding](https://mlsanity.com/paper/2210.12308/) (2022)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)
- [Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion](https://mlsanity.com/paper/2204.10936/) (2022)
- [Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions](https://mlsanity.com/paper/2412.10933/) (2024)
- [Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations](https://mlsanity.com/paper/2108.13810/) (2021)
- [Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems](https://mlsanity.com/paper/2507.05940/) (2025)
- [OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations](https://mlsanity.com/paper/2605.23668/) (2026)
- [Deep Natural Language Processing for LinkedIn Search Systems](https://mlsanity.com/paper/2108.08252/) (2021)
- [Entity-Centric Query Refinement](https://mlsanity.com/paper/2204.00743/) (2022)
- [Improving Sequential Query Recommendation with Immediate User Feedback](https://mlsanity.com/paper/2205.06297/) (2022)
- [DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion](https://mlsanity.com/paper/2604.18257/) (2026)
- [Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling](https://mlsanity.com/paper/2605.05855/) (2026)
- [Semantic Retrieval for Product Search in E-Commerce](https://mlsanity.com/paper/2606.01504/) (2026)

## Abstract (arXiv)

Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.