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.

翻译：会话搜索是一项具有挑战性的任务，其目标不仅基于当前用户查询，还需结合完整的对话历史来检索相关文档。先前的研究大多集中于依赖查询重构的多阶段排序方法，这一关键中间步骤可能导致检索效果次优。其他方法尝试采用完全神经信息检索的一阶段排序，但要么是零样本方式，要么依赖于基于伪标签数据集的完整学习排序。本研究利用CANARD数据集，提出了一种创新的轻量级学习技术，用于训练基于SPLADE的一阶段排序器。通过采用SPLADE稀疏表示，我们证明当与基于T5Mono的二阶段排序器结合时，在TREC CAsT 2020和2021赛道中取得了具有竞争力的结果。