In contrast to conventional teaching agents (including robots) that were designed to play the role of human teachers or caregivers, we propose the opposite scenario in which robots receive instruction or care from children. We hypothesize that by using this care-receiving robot, we may construct a new educational framework whose goal is to promote children's spontaneous learning by teaching through their teaching the robot. In this paper, we describe the introduction of a care-receiving robot into a classroom at an English language school for Japanese children (3--6 years of age) and then conduct an experiment to evaluate if the care-receiving robot can promote their learning using English verbs. The results suggest that the idea of a care-receiving robot is feasible and that the robot can help children learn new English verbs efficiently. In addition, we report on investigations into several forms of teaching performed by children, which were revealed through observations of the children, parent interviews, and other useful knowledge. These can be used to improve the design of care-receiving robots for educational purposes.

Children teach a care-receiving robot to promote their learning: field experiments in a classroom for vocabulary learning

In recent years, robots have increasingly been implemented as tutors in both first- and second-language education. The field of robot-assisted language learning (RALL) is developing rapidly. Studie...

https://journals.sagepub.com/doi/pdf/10.3102/0034654318821286

Social Robots for Language Learning: A Review:

Transformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and does not overtrain easily. However, it does have a serious drawback: the training time is often intorelably long, especially on the large corpora which are often used in NLP. In this paper, we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacrificing performance. The paper compares and contrasts the training time needed and performance achieved by our modified learner with two other systems: a standard transformation-based learner, and the ICA system \cite{hepple00:tbl}. The results of these experiments show that our system is able to achieve a significant improvement in training time while still achieving the same performance as a standard transformation-based learner. This is a valuable contribution to systems and algorithms which utilize transformation-based learning at any part of the execution.

Transformation-Based Learning in the Fast Lane

An educational use of Pepper, a personal robot that was developed by SoftBank Robotics Corp. and Aldebaran Robotics SAS, is described. Applying the two concepts of care-receiving robot (CRR) and total physical response (TPR) into the design of an educational application using Pepper, we offer a scenario in which children learn together with Pepper at their home environments from a human teacher who gives a lesson from a remote classroom. This paper is a case report that explains the developmental process of the application that contains three educational programs that children can select in interacting with Pepper. Feedbacks and knowledge obtained from test trials are also described.

/pdf/pepper-learns-together-with-children-development-of-an-187gyu2h6i.pdf

Pepper learns together with children: Development of an educational application

This study aimed to examine the effect of robot assisted language learning (RALL) on the anxiety level and attitude in English vocabulary acquisition amongst Iranian EFL junior high school students. Forty-six female students, who were beginners at the age of 12, participated in this study and were randomly assigned into two groups of RALL (30 students) and non-RALL (16 students). The textbook, the materials, as well as the teacher were the same in the two groups. However in the RALL group, the treatment was given by a teacher accompanied by a humanoid robot assistant. Two questionnaires of anxiety and attitude were utilized to measure the students’ anxiety and attitude (Horwitz et al. 1986; Alemi and Alipour 2012). The results of descriptive and t tests indicated that there was lower anxiety and a more positive attitude towards English vocabulary acquisition in the RALL group compared with those in the non-RALL group. The study showed that the students in the RALL group had great fun in the learning process; they also believed they were learning more effectively, which helped them boost their motivation in the long run. The present study provides new insights into the use of technology in the language classroom, suggesting that teachers and material developers should integrate technology into the language learning process.

The Impact of Social Robotics on L2 Learners’ Anxiety and Attitude in English Vocabulary Acquisition

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 pre-test/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.

On the effectiveness of robot-assisted language learning

While extensive studies on relation extraction have been conducted in the last decade, statistical systems based on supervised learning are still limited because they require large amounts of training data to achieve high performance. In this paper, we develop a cross-lingual annotation projection method that leverages parallel corpora to bootstrap a relation detector without significant annotation efforts for a resource-poor language. In order to make our method more reliable, we introduce three simple projection noise reduction methods. The merit of our method is demonstrated through a novel Korean relation detection task.

/pdf/a-cross-lingual-annotation-projection-approach-for-relation-4sd6yuawr6.pdf

A Cross-lingual Annotation Projection Approach for Relation Detection

In this paper, we describe several techniques to improve Korean-English statistical machine translation. We have built a phrase-based statistical machine translation system in a travel domain. On the baseline phrase-based system, several techniques are applied to improve the translation quality. Each technique can be applied or removed easily since the techniques are part of the preprocessing method or corpus processing method. Our experiments show that most of the techniques were successful except reordering the word sequence. The combination of the successful techniques has significantly improved the translation quality. Index Terms: statistical machine translation

/pdf/improving-phrase-based-korean-english-statistical-machine-33pth66krq.pdf

Improving Phrase-based Korean-English Statistical Machine Translation

Although researchers have conducted extensive studies on relation extraction in the last decade, statistical systems based on supervised learning are still limited, because they require large amounts of training data to achieve high performance level. In this article, we propose cross-lingual annotation projection methods that leverage parallel corpora to build a relation extraction system for a resource-poor language without significant annotation efforts. To make our method more reliable, we introduce two types of projection approaches with noise reduction strategies. We demonstrate the merit of our method using a Korean relation extraction system trained on projected examples from an English-Korean parallel corpus. Experiments show the feasibility of our approaches through comparison to other systems based on monolingual resources.

Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction

We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors. A series of experiments is presented to show the effectiveness of the proposed approach on two different grammatical error tagged corpora.

A Meta Learning Approach to Grammatical Error Correction

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