MLIA-DAC@TREC CAsT 2022: Sparse Contextualized Query Embedding

We extend SPLADE, a sparse information retrieval model, as our first stage ranker for the conversational task. This end-to-end approach achieves a high recall (as measure on TREC CAsT 2021). To further increase the effectiveness of our approach, we train a T5-based re-ranker. This working note fully describes our model and the four runs submitted to TREC CAsT 2022.