A Corpus-Based Study on Feedback in Daily Conversation: Forms, Position and Contexts.

Abstract

Understanding how feedback is employed in various forms, positions, and contexts can provide valuable insights into improving communication and the design of human-machine dialogue systems. This paper aims to deepen the understanding of feedback in daily conversation and investigate how feedback is employed in various linguistic forms, position, preceding and following contexts, using a large corpus of telephone conversations. The study identifies three subclasses of feedback, including understandings, agreements, and answers, which account for almost one-third of the total utterances in the corpus. Acknowledge (backchannel) is the most frequently used subtype of feedback, accounting for almost 60% of the feedback, and is primarily used for conversational management and maintenance. Assessment/appreciation, on the other hand, is used less frequently, accounting for less than 10% of feedback, and is mainly realized by more creative, unpredictable, longer forms. The analysis also reveals that speakers are intentional in distinguishing the three subclasses of feedback based on various variables, such as position and the proximal discourse environment. Furthermore, the three subclasses of feedback are restricted by the function of preceding contexts, which shape the length of the remaining turn. The study suggests that future research should focus on exploring the individual differences and investigating the possible variations across different cultures and languages.