Exploration of Human Repair Initiation in Task-oriented Dialogue: A Linguistic Feature-based Approach (2024)

Abstract: In daily conversations, people often encounter problems prompting conversational repair to enhance mutual understanding. By employing an automatic coreference solver, alongside examining repetition, we identify various linguistic features that distinguish turns when the addressee initiates repair from those when they do not. Our findings reveal distinct patterns that characterize the repair sequence and each type of repair initiation.

## introduction human language complexities often expose flaws such as misunderstandings, misinterpretations, speech impediments, or social norm violations. strategies people use in conversations to identify and address these problems, fostering mutual understanding, are called repair @xcite . schegloff, 2007 distinguishes repair types based on who initiates and who provides the solution between the speaker and the addressee. this paper focuses on other-initiated self-repair, also called other-initiated repair (oir), where the addressee initiates repair for the speaker corrects, as highlighted by @xcite as foundational for human language resilience, complexity, and flexibility. recent studies emphasize the need for conversational agents (cas) to have repair mechanisms. @xcite show that museum guide robots failing to promptly address issues led to visitor disengagement and conversation breakdowns, suggesting the importance of multimodal repair strategies. @xcite find that simple oir mechanisms in agents improve communicative success and reduce computational and interaction costs for disambiguation in communication compared to pragmatic reasoning (interlocutors reason each other). efforts to detect oir in the literature are narrow. @xcite trained a supervised classifier on four different datasets using turn-level features extracted from transcription, such as numbers of wh-words and fillers. the results indicate that challenging repairs are more common in taskoriented datasets. besides, research on integrating oir in cas is limited and primarily relies on rulebased systems. for instance, höhn, 2017 developed a rule-based chatbot with repair capabilities that recognize repair initiation in messaging conversations using conversational analysis rules, such as repetition, determiner and pronoun usage, and adjacent position. example 1. sample of oir sequence, annotated based on dingemanse and enfield, 2015's coding schema. data is in dutch, english translation provided by deepl foot_0 . ts speaker: en ik zie een uh ovaalvormig ding op het kopje (t-1) (and i see a uh oval-shaped thing on the cup) repair initiator: op het platte kopje daarboven hè? (t0) (on the flat head up there huh?) ts speaker: ja (t+1) (yes) a minimal oir sequence comprises three components: trouble source (ts) turn (t-1), repair initiation (t0), and repair solution (t+1), depicted in example 1. t-1 is where a potential communication problem arises, t0 is where the addressee signals a problem, and t+1 is where the speaker resolves the problem, completing the repair sequence. in addition, dingemanse and enfield, 2015 categorized repair initiation into three types: open request (the least specific, no ts specified in t-1), restricted request (implying the ts location), and restricted offer (the most specific, proposing a candidate understanding). our research aims to develop a ca system that can detect human repair initiation (t0) based on verbal and non-verbal cues and generate an appropriate repair solution (t+1). this work examines dialogue transcripts to identify linguistic features that distinguish oir sequences from nonrepair sequences and differentiate among the three types of oir in task-oriented dialogues. previous studies have identified various oir practices, for instance, @xcite described five oir formats in their study of american english conversation, while dingemanse et al., 2014 find similarities in oir formats across ten languages, such as question word "what?" or interjection "huh." the contributions of this paper are as follows: first, unlike previous studies that focused solely on references to trouble sources (ts), this work expands further by examining the acceptance of repair initiation by subsequent turn. second, in addition to repetition, we incorporate an automatic coreference solver to see if repair initiators refer back to the ts and if the response acknowledges the repair initiation. results show significant coreference involvement in restricted request and restricted offer. ## dataset as repair occurs more often in task-oriented dialogue and is generally unaffected by familiarity or interaction mode @xcite , we employ dialogue transcripts from a dutch multimodal task-oriented corpus @xcite within project cabb @xcite , involving 20 dyads performing referential communication tasks to locate 16 stimulated geometrical objects called fribbles. the data collection setup corresponds to the cabb dataset, described in @xcite . participants alternated between director and matcher roles to communicate and locate specified objects. each participant's speech was segmented into turn constructional units (tcus) and then orthographically transcribed based on standard spelling conventions of dutch. the repair sequences were annotated following dingemanse and enfield, 2015's coding schema, resulting in: 20 (open request), 32 (restricted request), and 255 (restricted offer) sequences, respectively. we examine the interaction differences after a potential issue (turn labeled as ts) to compare instances when a person initiates repair versus when they do not identify trouble and request repair immediately. to do this, we selected all turns between t-1 and the repair initiation in t0, identifying 91 non-repair sequences. appendix a provides sample data for each oir type and details the nonrepair selection method. ## feature extraction based on the oir coding schema (example 1), t0 is considered a repair initiation if it (1) treats the prior turn containing trouble and (2) the subsequent turn t+1 acknowledges and responds to this request. to determine (1), we analyze the syntactic structure of the repair initiation turn (t0) regarding the potential ts in t-1 via coreferences. for (2), we examine how the ts speaker acknowledges the repair initiation by analyzing coreferences in t+1 that refer to the entity mentioned in t0 and the ts speaker's self-repetition. ## conclusion and future work utilizing natural language processing approaches on dialogue transcripts, we identified linguistic and sequential patterns characterizing three types of oir and non-repair sequences. the coreference chains used in t0 combined with sequential patterns of oir structure are typical across oir types and non-repair sequences, which reveal the grammatical structure of t0 and whether t0 treats the prior turn t-1 as containing trouble. besides, the ts speaker's self-repetition and coreference chains (initiated by the repair initiator) used in t+1 show the behavior of the ts speaker in acceptance of the repair initiation from t0. our future work will explore multimodalities like prosodic, facial and bodily cues, to develop a computational model for repair initiation detection and repair solution generation in the conversational agent. codi-crac 2022 shared task on anaphora, bridging, and discourse deixis in dialogue, pages 1-14, gyeongju, republic of korea. association for computational linguistics.