Improving Conversational Passage Retrieval through Mixed-Initiative Query Reformulation

Основные понятия

Incorporating mixed-initiative interaction into conversational passage retrieval systems can improve retrieval performance by resolving ambiguities in raw queries.

Аннотация

The paper presents a multi-stage retrieval pipeline for conversational passage retrieval that includes a mixed-initiative query reformulation module. The key highlights are:

The multi-stage pipeline consists of four stages: query reformulation, first ranking, re-ranking, and fusion.

For the query reformulation stage, the authors propose a mixed-initiative approach that generates clarifying questions to resolve ambiguities in raw queries, and then reformulates the queries based on user feedback.

The mixed-initiative query reformulation module is designed to identify three types of ambiguities in raw queries: incomplete, reference, and descriptive. It generates corresponding questions to seek clarification from users.

Experiments on the TREC CAsT 2021 and 2022 datasets show that the mixed-initiative approach outperforms using neural (CANARD-T5) or rule-based (HQE) query reformulators alone.

The authors also explore techniques to improve the query reformulation stage, such as fine-tuning the T5 rewriter on previous TREC CAsT datasets and fusing multiple top-probable outputs from the generative reformulator.

The paper concludes that incorporating mixed-initiative interaction into conversational passage retrieval systems has the potential to improve retrieval performance.

Статистика

The paper reports the following key metrics:

Recall@1000, Recall@500, MAP@1000, NDCG@3 on the TREC CAsT 2021 and 2022 datasets.
Comparison of retrieval performance between using the original T5 rewriter and the fine-tuned T5 rewriter.
Comparison of retrieval performance when considering different numbers of canonical passages in the T5 rewriter.
Improvement in Recall@500, MAP@500, NDCG@500, NDCG@3 when fusing multiple top-probable outputs from the generative reformulator.
Improvement in Recall@500, Recall@1000, Recall@3000 when tuning the BM25 parameters.

Цитаты

"Incorporating mixed-initiative interaction into conversational passage retrieval systems has the potential to improve retrieval performance."
"The mixed-initiative query reformulation module is designed to identify three types of ambiguities in raw queries: incomplete, reference, and descriptive."
"Experiments on the TREC CAsT 2021 and 2022 datasets show that the mixed-initiative approach outperforms using neural (CANARD-T5) or rule-based (HQE) query reformulators alone."

Ключевые выводы из

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

by Dayu Yang,Yu... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2307.08803.pdf

Дополнительные вопросы

How can the mixed-initiative query reformulation module be further improved to handle a wider range of ambiguities in raw queries?

The mixed-initiative query reformulation module can be enhanced by incorporating more sophisticated natural language processing techniques. One approach could involve leveraging contextual embeddings or transformer models to better understand the nuances in user queries and generate more accurate reformulations. By training the system on a diverse range of query types and user interactions, it can learn to identify and address a broader spectrum of ambiguities, including complex references, implicit meanings, and domain-specific terminology. Additionally, integrating feedback mechanisms that allow users to provide more detailed responses or corrections can help refine the reformulation process further.

What are the potential drawbacks or limitations of the mixed-initiative approach, and how can they be addressed?

One potential limitation of the mixed-initiative approach is the reliance on user input, which can introduce delays and uncertainties in the reformulation process. Users may provide incomplete or inaccurate responses, leading to suboptimal query reformulations. To address this, the system can implement strategies to verify and validate user feedback, such as prompting for clarifications or offering multiple-choice options for responses. Moreover, incorporating machine learning algorithms that can adapt and learn from user interactions over time can help improve the accuracy and efficiency of the mixed-initiative query reformulation module.

How can the insights from this work on conversational passage retrieval be applied to other information retrieval tasks that involve user interaction?

The insights gained from this work on conversational passage retrieval can be extrapolated to various other information retrieval tasks that involve user interaction, such as chatbots, question-answering systems, and personalized recommendation engines. By integrating mixed-initiative approaches, these systems can better understand user intent, address ambiguities in queries, and provide more relevant and tailored responses. Additionally, the multi-stage retrieval pipeline architecture and the use of diverse ranking methods can be adapted to different domains to enhance the performance of information retrieval systems. Overall, the principles of mixed-initiative interaction, query reformulation, and multi-stage retrieval can be applied to create more effective and user-centric information retrieval solutions across a wide range of applications.