English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we report our methods and experiments for the TRECConversational Assistance Track (CAsT) 2022. In this work, we aim to reproducemulti-stage retrieval pipelines and explore one of the potential benefits ofinvolving mixed-initiative interaction in conversational passage retrievalscenarios: reformulating raw queries. Before the first ranking stage of amulti-stage retrieval pipeline, we propose a mixed-initiative queryreformulation module, which achieves query reformulation based on themixed-initiative interaction between the users and the system, as thereplacement for the neural reformulation method. Specifically, we design analgorithm to generate appropriate questions related to the ambiguities in rawqueries, and another algorithm to reformulate raw queries by parsing users'feedback and incorporating it into the raw query. For the first ranking stageof our multi-stage pipelines, we adopt a sparse ranking function: BM25, and adense retrieval method: TCT-ColBERT. For the second-ranking step, we adopt apointwise reranker: MonoT5, and a pairwise reranker: DuoT5. Experiments on bothTREC CAsT 2021 and TREC CAsT 2022 datasets show the effectiveness of ourmixed-initiative-based query reformulation method on improving retrievalperformance compared with two popular reformulators: a neural reformulator:CANARD-T5 and a rule-based reformulator: historical query reformulator(HQE).

An Exploration Study of Mixed-initiative Query Reformulation in Conversational Passage Retrieval