This research focuses on exploring methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems have made significant advancements, incorporating user feedback remains a challenge due to the lack of system-user conversational interaction data. To address this, the researchers propose a user simulator-based framework called ConvSim. This simulator is capable of providing feedback to system responses and answering clarifying questions. The experiments conducted on various state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to a 16% increase in retrieval performance in terms of nDCG@3. Additionally, consistent improvements are observed as the number of feedback rounds increases, with a 35% relative improvement after three rounds. This highlights the need for specific feedback processing modules and presents opportunities for advancements in CS. To support further research in this area, the researchers release over 30,000 transcripts of system-simulator interactions based on established CS datasets. The study also identifies shortcomings in existing methods and proposes solutions. For example, standard T5 query rewriter struggles with processing feedback, but a novel adaptation of the T5 method leads to improvements in recall and nDCG@3 by 10% and 16%, respectively. Incorporating answers to clarifying questions also yields improvements in recall (18%) and nDCG@3 (12%). Furthermore, it is found that multiple rounds of simulator-system interactions result in further enhancements in retrieval effectiveness. To overcome the lack of research on feedback utilization and appropriate data, the researchers develop an experimental framework based on simulated user-system interactions. This framework allows for evaluating multiple mixed-initiative CS systems and addressing challenges such as contextual query resolution, asking clarifying questions, and incorporating user feedback. Overall, this study contributes to advancing the field of conversational search by demonstrating the benefits of utilizing user feedback and providing a comprehensive experimental framework for future research.
- Research focuses on assessing user feedback in mixed-initiative conversational search (CS) systems
- Proposed framework called ConvSim uses a user simulator to provide feedback and answer clarifying questions
- Effective utilization of user feedback leads to a 16% increase in retrieval performance
- Increasing the number of feedback rounds results in a 35% relative improvement after three rounds
- Over 30,000 transcripts of system-simulator interactions are released for further research
- Existing methods have shortcomings, but novel adaptations lead to improvements in recall and nDCG@3
- Incorporating answers to clarifying questions improves recall and nDCG@3
- Multiple rounds of simulator-system interactions enhance retrieval effectiveness
- Experimental framework based on simulated user-system interactions addresses challenges in CS systems
Research focuses on studying how people give feedback and ask questions when using a computer program that helps them find information.
A proposed framework called ConvSim uses a pretend person to give feedback and answer questions in order to make the computer program better.
When people's feedback is used effectively, the computer program can find information 16% better than before.
If more rounds of feedback are given, the computer program can improve by 35% after three rounds.
Researchers have released over 30,000 examples of how people interact with the computer program for other scientists to study.
Definitions
- Research: The process of studying something to learn new things.
- Feedback: Information or opinions given to help improve something.
- Framework: A plan or structure for doing something.
- Simulator: Something that imitates or pretends to be real.
- Retrieval performance: How well a computer program can find information.
- Rounds: Times when something happens again and again in a sequence.
- Transcripts: Written records of what was said or done during an interaction.
- Interactions: When two or more things affect each other by communicating or working together.
Exploring Methods for Assessing User Feedback in Mixed-Initiative Conversational Search Systems
The development of conversational search (CS) systems has made significant advancements, but incorporating user feedback remains a challenge due to the lack of system-user interaction data. To address this issue, researchers from the University of Maryland have proposed a user simulator-based framework called ConvSim that is capable of providing feedback to system responses and answering clarifying questions. Experiments conducted on various state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to an increase in retrieval performance. Additionally, consistent improvements are observed as the number of feedback rounds increases. This highlights the need for specific feedback processing modules and presents opportunities for advancements in CS.
Background
Mixed-initiative CS systems allow users to interact with a search engine by asking natural language queries and providing additional information when needed. These systems are designed to understand complex queries, provide relevant results, and incorporate user feedback into subsequent searches. However, due to the lack of available data on system-user interactions, it is difficult to assess how well these systems perform when given user input or how they should be modified accordingly.
Proposed Framework: ConvSim
To overcome this limitation, the researchers developed an experimental framework based on simulated user–system interactions using ConvSim – a novel approach for assessing mixed initiative CS systems with real human input data. The framework allows for evaluating multiple CS models while addressing challenges such as contextual query resolution, asking clarifying questions, and incorporating user feedback into subsequent searches.
The experiments conducted on various state-of-the art passage retrieval and neural reranking models demonstrate that effective utilization of user feedback leads to improved performance in terms of nDCG@3 (normalized discounted cumulative gain). Specifically, after three rounds of simulator–system interactions there was a 35% relative improvement compared with baseline results without any simulated human input data. Furthermore, standard T5 query rewriter struggled with processing feedback; however a novel adaptation led to 10% recall improvement and 16% nDCG@3 improvement over baseline results without any simulated human input data . Incorporating answers from clarifying questions also yielded improvements in recall (18%) and nDCG@3 (12%). Multiple rounds resulted in further enhancements in retrieval effectiveness which suggests that more research needs to be done on appropriate methods for incorporating user input into conversational search engines..
Data Release
To support further research in this area ,the researchers released over 30 000 transcripts from their experiments which were based established CS datasets such as MS MARCO v1/v2 ,TREC CAR ,and ClueWeb09b .These transcripts contain both system responses as well as corresponding simulator inputs including initial queries ,feedback ,clarification questions etc .This will enable other researchers interested in exploring mixed initiative conversational search techniques access high quality datasets containing real human inputs .
Conclusion
Overall ,this study contributes significantly towards advancing the field by demonstrating benefits associated with utilizing user feedback along with providing comprehensive experimental framework which can be used by other researchers interested exploring mixed initiative conversational search techniques .
