# CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval

arXiv: https://arxiv.org/abs/2301.04413 · HTML: https://mlsanity.com/paper/2301.04413/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering](https://mlsanity.com/paper/2503.22303/) (2025)
- [Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues](https://mlsanity.com/paper/2410.03049/) (2024)
- [STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation](https://mlsanity.com/paper/2609.03874/) (2026)

## Abstract (arXiv)

Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previous methods have focused on a multi-stage ranking approach relying on query reformulation, a critical intermediate step that might lead to a sub-optimal retrieval. Other approaches have tried to use a fully neural IR first-stage, but are either zero-shot or rely on full learning-to-rank based on a dataset with pseudo-labels. In this work, leveraging the CANARD dataset, we propose an innovative lightweight learning technique to train a first-stage ranker based on SPLADE. By relying on SPLADE sparse representations, we show that, when combined with a second-stage ranker based on T5Mono, the results are competitive on the TREC CAsT 2020 and 2021 tracks.