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Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search

Core Concepts
The author explores ways to rewrite answers in Conversational Information Seeking (CIS) by focusing on salient entities, aiming to improve user experience. Two strategies are proposed: inline definitions of salient entities and follow-up questions.
Conversational Information Seeking (CIS) introduces challenges due to limited bandwidth interfaces. The study focuses on entity salience and proposes two answer rewriting strategies. Results show a preference for rewritten answers with inline explanations over original ones. Traditional web search allows users to explore concepts through hyperlinks or knowledge panels, which is limited in CIS. Salient entities are crucial for understanding answers in CIS, prompting the need for personalized answer rewriting strategies. Inline definitions and follow-up questions are proposed as effective methods for improving user experience. Crowdsourcing-based evaluations reveal a preference for rewritten answers with inline explanations, indicating the potential of entity-based answer rewriting in enhancing user understanding and engagement in conversational search settings.
The average salience score of entities is 1.24 ± 0.33. On average, 63% of all entities in CIS can be considered salient. The average change of saliency score between two consecutive turns is 0.36 ± 0.21.
"Do you want to learn more about specific entities?" "Rewritten answers with inline definitions are preferred over original ones." "Salient entities play a crucial role in improving user experience."

Key Insights Distilled From

by Ivan... at 03-05-2024
Towards Self-Contained Answers

Deeper Inquiries

How can background knowledge influence preferences for answer rewrites?

Background knowledge plays a crucial role in influencing preferences for answer rewrites in conversational information-seeking systems. Users with varying levels of expertise or familiarity with the topic at hand may have different needs and expectations when it comes to understanding the provided answers. Level of Understanding: Users with a strong background in the subject matter may prefer concise answers that assume a certain level of prior knowledge, as they might find detailed explanations redundant or patronizing. On the other hand, users who are less familiar with the topic may appreciate more detailed explanations to help them grasp the concepts better. Personal Preferences: Background knowledge also shapes personal preferences regarding how information is presented. Some users might prefer straightforward and direct answers, while others may enjoy additional context or elaboration on key terms or concepts. Desired Depth of Information: Users' background knowledge influences their desired depth of information in an answer rewrite. Those well-versed in a topic might seek advanced insights or nuanced details, while novices may require basic definitions and explanations to build foundational understanding. Relevance and Context: Background knowledge helps users assess the relevance and accuracy of provided information. Users with relevant expertise can quickly identify inaccuracies or gaps in content, leading them to favor accurate and comprehensive answer rewrites aligned with their existing knowledge base. In summary, background knowledge significantly impacts user preferences for answer rewrites by shaping their expectations regarding clarity, depth, relevance, and presentation style based on their familiarity with the subject matter.

What implications does the high subjectivity of rewrite preferences have on system design?

The high subjectivity observed in user preferences for answer rewites has several implications on system design: Personalization Strategies: System designers need to implement personalized approaches that consider individual differences in user backgrounds, expertise levels, learning styles, and preferred modes of receiving information. Adaptive Content Delivery: Systems should dynamically adjust response formats based on inferred user profiles or explicit feedback to cater to diverse preferences effectively. User Profiling: Incorporating mechanisms for capturing user characteristics such as domain expertise levels through profiling can enhance recommendation algorithms tailored towards individualized rewriting strategies. 4 .Interactive Feedback Mechanisms: Providing interactive features like feedback loops where users can express their satisfaction levels with specific types of responses enables continuous improvement based on real-time input from users. 5 .A/B Testing: Conducting A/B testing experiments allows system designers to evaluate various rewriting strategies under different conditions and refine approaches based on empirical data rather than assumptions about user preference.

How might voice-only settings impact user preferences for answer rewrites?

Voice-only settings could significantly impact user preferences for answer rewrites due to differences in interaction modalities compared to text-based interfaces: 1 .Auditory Processing Constraints: In voice-only settings, users rely solely on auditory cues without visual aids like text formatting or inline definitions present in written responses which could affect comprehension rates especially when dealing complex topics requiring detailed explanations 2 .Cognitive Load Considerations: Voice interactions impose constraints related cognitive load management since listeners must process spoken content linearly without being able skim through parts unlike reading text which allows non-linear navigation potentially impacting preference towards succinct vs elaborate responses 3 .Engagement Dynamics: Voice interactions offer opportunities enhanced engagement through natural language processing capabilities enabling more fluid conversations but excessive verbosity could lead disengagement hence affecting preference towards concise yet informative responses 4 .Contextual Prompting Challenges: In voice-only scenarios prompting follow-up questions explaining salient entities becomes challenging given limitations sequential nature conversation flow thus necessitating careful crafting prompts avoid disrupting dialogue flow while ensuring essential clarifications delivered appropriately