Utku Norman, Tanvi Dinkar, Barbara Bruno, C. Clavel
{"title":"通过自动方法研究协作学习活动中的一致性:我们所说和所做之间的联系","authors":"Utku Norman, Tanvi Dinkar, Barbara Bruno, C. Clavel","doi":"10.5210/dad.2022.201","DOIUrl":null,"url":null,"abstract":"A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are evaluated on how well they performed and how much they learnt. Our main contribution is to propose new automatic measures to study alignment; focusing on lexical alignment, and a new alignment context that we introduce termed as behavioural alignment (when an instruction given by one interlocutor was followed with concrete actions in a physical environment by another). Thus we propose methodologies to create a link between what was said, and what was done as a consequence. To do so, we focus on expressions related to the task in the situated activity. These expressions are minimally required by the interlocutors to make progress in the task. We then observe how these local alignment contexts build to dialogue level phenomena; success in the task. What distinguishes our approach from other works, is the treatment of alignment as a procedure that occurs in stages. Since we utilise a dataset of spontaneous speech dialogues elicited from children, a second contribution of our work is to study how spontaneous speech phenomena (such as when interlocutors say \"uh\", \"oh\" ...) are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams lexically and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive, collaborative period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker \"oh\" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of \"oh\" as an information management marker in a behavioural context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. Our measures contribute to the research in the field of educational dialogue and the intersection between dialogue and collaborative learning research. ","PeriodicalId":37604,"journal":{"name":"Dialogue and Discourse","volume":"25 1","pages":"1-48"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do\",\"authors\":\"Utku Norman, Tanvi Dinkar, Barbara Bruno, C. Clavel\",\"doi\":\"10.5210/dad.2022.201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are evaluated on how well they performed and how much they learnt. Our main contribution is to propose new automatic measures to study alignment; focusing on lexical alignment, and a new alignment context that we introduce termed as behavioural alignment (when an instruction given by one interlocutor was followed with concrete actions in a physical environment by another). Thus we propose methodologies to create a link between what was said, and what was done as a consequence. To do so, we focus on expressions related to the task in the situated activity. These expressions are minimally required by the interlocutors to make progress in the task. We then observe how these local alignment contexts build to dialogue level phenomena; success in the task. What distinguishes our approach from other works, is the treatment of alignment as a procedure that occurs in stages. Since we utilise a dataset of spontaneous speech dialogues elicited from children, a second contribution of our work is to study how spontaneous speech phenomena (such as when interlocutors say \\\"uh\\\", \\\"oh\\\" ...) are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams lexically and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive, collaborative period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker \\\"oh\\\" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of \\\"oh\\\" as an information management marker in a behavioural context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. 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Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do
A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are evaluated on how well they performed and how much they learnt. Our main contribution is to propose new automatic measures to study alignment; focusing on lexical alignment, and a new alignment context that we introduce termed as behavioural alignment (when an instruction given by one interlocutor was followed with concrete actions in a physical environment by another). Thus we propose methodologies to create a link between what was said, and what was done as a consequence. To do so, we focus on expressions related to the task in the situated activity. These expressions are minimally required by the interlocutors to make progress in the task. We then observe how these local alignment contexts build to dialogue level phenomena; success in the task. What distinguishes our approach from other works, is the treatment of alignment as a procedure that occurs in stages. Since we utilise a dataset of spontaneous speech dialogues elicited from children, a second contribution of our work is to study how spontaneous speech phenomena (such as when interlocutors say "uh", "oh" ...) are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams lexically and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive, collaborative period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker "oh" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of "oh" as an information management marker in a behavioural context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. Our measures contribute to the research in the field of educational dialogue and the intersection between dialogue and collaborative learning research.
期刊介绍:
D&D seeks previously unpublished, high quality articles on the analysis of discourse and dialogue that contain -experimental and/or theoretical studies related to the construction, representation, and maintenance of (linguistic) context -linguistic analysis of phenomena characteristic of discourse and/or dialogue (including, but not limited to: reference and anaphora, presupposition and accommodation, topicality and salience, implicature, ---discourse structure and rhetorical relations, discourse markers and particles, the semantics and -pragmatics of dialogue acts, questions, imperatives, non-sentential utterances, intonation, and meta--communicative phenomena such as repair and grounding) -experimental and/or theoretical studies of agents'' information states and their dynamics in conversational interaction -new analytical frameworks that advance theoretical studies of discourse and dialogue -research on systems performing coreference resolution, discourse structure parsing, event and temporal -structure, and reference resolution in multimodal communication -experimental and/or theoretical results yielding new insight into non-linguistic interaction in -communication -work on natural language understanding (including spoken language understanding), dialogue management, -reasoning, and natural language generation (including text-to-speech) in dialogue systems -work related to the design and engineering of dialogue systems (including, but not limited to: -evaluation, usability design and testing, rapid application deployment, embodied agents, affect detection, -mixed-initiative, adaptation, and user modeling). -extremely well-written surveys of existing work. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers on discourse and dialogue and its associated fields, including computer scientists, linguists, psychologists, philosophers, roboticists, sociologists.