Robot Interaction Styles for Conversation Practice in Second Language Learning

Engwall, Olov; Lopes, José; Åhlund, Anna

doi:10.1007/s12369-020-00635-y

Cited by 37 publications

(39 citation statements)

References 26 publications

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“…We performed a user study in which Furhat conducted simplified conversations with two adult learners of Swedish. The topics were personal matters (hobbies, family, work, background), languages (e.g., similarities and differences between Swedish and the learners' first languages), Sweden or the news, all topics that occur frequently in language caf es (Engwall, Lopes, & Åhlund, 2020). The aim of the present study was to investigate how the robot's behaviour influences interaction and collaboration between the learners and between the learners and the robot.…”

Section: Motivation and Methodology For The User Study On Interactionmentioning

confidence: 99%

“…These four robot behaviours were based on a subset of interaction strategies of human moderators, determined through questionnaire responses by 105 moderators and observations of small language caf e conversations with three participants (Engwall et al, 2020). The selected behaviours were designed to differ along the two dimensions robot-learner initiative (from robot-led in Interviewer to learner-driven in Facilitator) and robot-learner focus (from robot-focused in Narrator to learner-focused in Interviewer/ Facilitator).…”

Section: Motivation and Methodology For The User Study On Interactionmentioning

confidence: 99%

“…As a thorough statistical analysis of the full questionnaire is presented in Engwall et al (2020), a binary analysis was instead performed to identify the most preferred robot behaviour, in general, and by different categories of learners. This analysis started by identifying conversations that were rated higher than the mean over all sessions by the 19 subjects who experienced all behaviours (m learning ¼3.20 and m dialogue ¼2.96).…”

Section: Methodology For Collecting and Analysing Learner Preferencesmentioning

confidence: 99%

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Interaction and collaboration in robot-assisted language learning for adults

Engwall

Lopes

2020

Computer Assisted Language Learning

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This article analyses how robot-learner interaction in robotassisted language learning (RALL) is influenced by the interaction behaviour of the robot. Since the robot behaviour is to a large extent determined by the combination of teaching strategy, robot role and robot type, previous studies in RALL are first summarised with respect to which combinations that have been chosen, the rationale behind the choice and the effects on interaction and learning. The goal of the summary is to determine a suitable pedagogical setup for RALL with adult learners, since previous RALL studies have almost exclusively been performed with children and youths. A user study in which 33 adult second language learners practice Swedish in three-party conversations with an anthropomorphic robot head is then presented. It is demonstrated how different robot interaction behaviours influence interaction between the robot and the learners and between the two learners. Through an analysis of learner interaction, collaboration and learner ratings for the different robot behaviours, it is observed that the learners were most positive towards the robot behaviour that focused on interviewing one learner at the time (highest average ratings), but that they were the most active in sessions when the robot encouraged learner-learner interaction. Moreover, the preferences and activity differed between learner pairs, depending on, e.g., their proficiency level and how well they knew the peer. It is therefore concluded that the robot behaviour needs to adapt to such factors. In addition, collaboration with the peer played an important part in conversation practice sessions to deal with linguistic difficulties or communication problems with the robot.

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Section: Motivation and Methodology For The User Study On Interactionmentioning

confidence: 99%

Section: Motivation and Methodology For The User Study On Interactionmentioning

confidence: 99%

Section: Methodology For Collecting and Analysing Learner Preferencesmentioning

confidence: 99%

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Interaction and collaboration in robot-assisted language learning for adults

Engwall

Lopes

2020

Computer Assisted Language Learning

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“…And we can imagine a range of robot possibilities that might, given technological advances, the right robot appearance, robot behavior, and the presence of appropriate contexts, compellingly express humanistic care. Examples could include forms of tender touching and responsiveness to being touched [ 91 ], AI-enabled learning about specific patients and their individuality, reassuring conversation [ 92 ], and telling a story [ 93 ] to a depressed or distressed person. A robot might even portray such human vulnerability as can intimate human fellowship.…”

Section: Robot Humanistic Carementioning

confidence: 99%

Robots and the Possibility of Humanistic Care

Coghlan

2021

Int J of Soc Robotics

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Care robots are likely to perform increasingly sophisticated caring activities that some will consider comforting and valuable. They will get increasingly humanlike and lifelike. This paper addresses the conceptual question: Even if robots can assist and ease people’s suffering, can such machines provide humanistic care? Arguably, humanistic care is the most humanly distinctive and deepest form of care there is. As such, it may be thought to show most starkly the gulf between human and robot caregiving. The paper argues that humanistic caregiving is indeed a distinctive form of ‘affective’ care dependent on certain uniquely human characteristics or aspects of our humanity which can provide a profound kind of comfort to suffering people. It then argues that there is an important conceptual sense in which robots cannot provide humanistic care. Nonetheless, the paper subsequently suggests that we may recognize a useful sense in which robots, of a suitably anthropomorphic type, can provide humanistic care. Robots might ‘express’ to people with physical, social, or emotional needs the kind of humanistic care that only human beings can provide but that sufferers can nonetheless receive comfort from precisely because of what is expressed to them. Although this sense of humanistic robot care is derivative from uniquely human care, and although it is wide open to social and ethical criticism, it is nonetheless an idea worth clarifying for anyone interested in the possibilities and limits of robot care.

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“…Previously collected audio recordings of wizard-of-Ozcontrolled robot-led conversations in L2 Swedish [15] were submitted to Google's Cloud Speech ASR in batch to get transcriptions of learner utterances and evaluate their correctness and intelligibility (Sect. 4).…”

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confidence: 99%

Is a Wizard-of-Oz Required for Robot-Led Conversation Practice in a Second Language?

Engwall

Lopes

Cumbal

2022

Int J of Soc Robotics

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The large majority of previous work on human-robot conversations in a second language has been performed with a human wizard-of-Oz. The reasons are that automatic speech recognition of non-native conversational speech is considered to be unreliable and that the dialogue management task of selecting robot utterances that are adequate at a given turn is complex in social conversations. This study therefore investigates if robot-led conversation practice in a second language with pairs of adult learners could potentially be managed by an autonomous robot. We first investigate how correct and understandable transcriptions of second language learner utterances are when made by a state-of-the-art speech recogniser. We find both a relatively high word error rate (41%) and that a substantial share (42%) of the utterances are judged to be incomprehensible or only partially understandable by a human reader. We then evaluate how adequate the robot utterance selection is, when performed manually based on the speech recognition transcriptions or autonomously using (a) predefined sequences of robot utterances, (b) a general state-of-the-art language model that selects utterances based on learner input or the preceding robot utterance, or (c) a custom-made statistical method that is trained on observations of the wizard’s choices in previous conversations. It is shown that adequate or at least acceptable robot utterances are selected by the human wizard in most cases (96%), even though the ASR transcriptions have a high word error rate. Further, the custom-made statistical method performs as well as manual selection of robot utterances based on ASR transcriptions. It was also found that the interaction strategy that the robot employed, which differed regarding how much the robot maintained the initiative in the conversation and if the focus of the conversation was on the robot or the learners, had marginal effects on the word error rate and understandability of the transcriptions but larger effects on the adequateness of the utterance selection. Autonomous robot-led conversations may hence work better with some robot interaction strategies.

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Robot Interaction Styles for Conversation Practice in Second Language Learning

Cited by 37 publications

References 26 publications

Interaction and collaboration in robot-assisted language learning for adults

Interaction and collaboration in robot-assisted language learning for adults

Robots and the Possibility of Humanistic Care

Is a Wizard-of-Oz Required for Robot-Led Conversation Practice in a Second Language?

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