Divesh Lala


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ERICA: An Empathetic Android Companion for Covid-19 Quarantine
Etsuko Ishii | Genta Indra Winata | Samuel Cahyawijaya | Divesh Lala | Tatsuya Kawahara | Pascale Fung
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface: a web-based virtual agent, Nora vs. the android ERICA via a video call. The experimental results show that the android can offer a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.

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A multi-party attentive listening robot which stimulates involvement from side participants
Koji Inoue | Hiromi Sakamoto | Kenta Yamamoto | Divesh Lala | Tatsuya Kawahara
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

We demonstrate the moderating abilities of a multi-party attentive listening robot system when multiple people are speaking in turns. Our conventional one-on-one attentive listening system generates listener responses such as backchannels, repeats, elaborating questions, and assessments. In this paper, additional robot responses that stimulate a listening user (side participant) to become more involved in the dialogue are proposed. The additional responses elicit assessments and questions from the side participant, making the dialogue more empathetic and lively.


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An Attentive Listening System with Android ERICA: Comparison of Autonomous and WOZ Interactions
Koji Inoue | Divesh Lala | Kenta Yamamoto | Shizuka Nakamura | Katsuya Takanashi | Tatsuya Kawahara
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We describe an attentive listening system for the autonomous android robot ERICA. The proposed system generates several types of listener responses: backchannels, repeats, elaborating questions, assessments, generic sentimental responses, and generic responses. In this paper, we report a subjective experiment with 20 elderly people. First, we evaluated each system utterance excluding backchannels and generic responses, in an offline manner. It was found that most of the system utterances were linguistically appropriate, and they elicited positive reactions from the subjects. Furthermore, 58.2% of the responses were acknowledged as being appropriate listener responses. We also compared the proposed system with a WOZ system where a human operator was operating the robot. From the subjective evaluation, the proposed system achieved comparable scores in basic skills of attentive listening such as encouragement to talk, focused on the talk, and actively listening. It was also found that there is still a gap between the system and the WOZ for more sophisticated skills such as dialogue understanding, showing interest, and empathy towards the user.

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Designing Precise and Robust Dialogue Response Evaluators
Tianyu Zhao | Divesh Lala | Tatsuya Kawahara
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models. Experimental results demonstrate that the proposed evaluator achieves a strong correlation (> 0.6) with human judgement and generalizes robustly to diverse responses and corpora. We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.


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Attentive listening system with backchanneling, response generation and flexible turn-taking
Divesh Lala | Pierrick Milhorat | Koji Inoue | Masanari Ishida | Katsuya Takanashi | Tatsuya Kawahara
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Attentive listening systems are designed to let people, especially senior people, keep talking to maintain communication ability and mental health. This paper addresses key components of an attentive listening system which encourages users to talk smoothly. First, we introduce continuous prediction of end-of-utterances and generation of backchannels, rather than generating backchannels after end-point detection of utterances. This improves subjective evaluations of backchannels. Second, we propose an effective statement response mechanism which detects focus words and responds in the form of a question or partial repeat. This can be applied to any statement. Moreover, a flexible turn-taking mechanism is designed which uses backchannels or fillers when the turn-switch is ambiguous. These techniques are integrated into a humanoid robot to conduct attentive listening. We test the feasibility of the system in a pilot experiment and show that it can produce coherent dialogues during conversation.


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Talking with ERICA, an autonomous android
Koji Inoue | Pierrick Milhorat | Divesh Lala | Tianyu Zhao | Tatsuya Kawahara
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue