This study introduces the educational assistant robots that we developed for foreign language learning and explores the effectiveness of robot-assisted language learning (RALL) which is in its early stages. To achieve this purpose, a course was designed in which students have meaningful interactions with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age from ten to twelve, were enrolled in English lessons. A pretest/post-test design was used to investigate the cognitive effects of the RALL approach on the students' oral skills. No significant difference in the listening skill was found, but the speaking skills improved with a large effect size at the significance level of 0.01. Descriptive statistics and the pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL promoted and improved students' satisfaction, interest, confidence, and motivation at the significance level of 0.01.
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoderdecoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation. 1
Generative encoder-decoder models offer great promise in developing domaingeneral dialog systems. However, they have mainly been applied to open-domain conversations.This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slotfilling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.
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