Towards Teachable Conversational Agents
- URL: http://arxiv.org/abs/2102.10387v1
- Date: Sat, 20 Feb 2021 16:56:24 GMT
- Title: Towards Teachable Conversational Agents
- Authors: Nalin Chhibber, Edith Law
- Abstract summary: We explore the idea of using a conversational interface to investigate the interaction between human-teachers and interactive machine-learners.
Results validate the concept of teachable conversational agents and highlight the factors relevant for the development of machine learning systems that intend to learn from conversational interactions.
- Score: 9.003996147141919
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The traditional process of building interactive machine learning systems can
be viewed as a teacher-learner interaction scenario where the machine-learners
are trained by one or more human-teachers. In this work, we explore the idea of
using a conversational interface to investigate the interaction between
human-teachers and interactive machine-learners. Specifically, we examine
whether teachable AI agents can reliably learn from human-teachers through
conversational interactions, and how this learning compare with traditional
supervised learning algorithms. Results validate the concept of teachable
conversational agents and highlight the factors relevant for the development of
machine learning systems that intend to learn from conversational interactions.
Related papers
- Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework [13.949126295663328]
We bridge the gap between machine learning and human-computer interaction efforts by developing a shared understanding of human feedback in interactive learning scenarios.
We introduce a taxonomy of feedback types for reward-based learning from human feedback based on nine key dimensions.
We identify seven quality metrics of human feedback influencing both the human ability to express feedback and the agent's ability to learn from the feedback.
arXiv Detail & Related papers (2024-11-18T17:40:42Z) - Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking [10.363782876965221]
We envision a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences.
The insights obtained in this paper can guide future implementations of tutoring agents oriented toward teaching computational thinking and computer programming.
arXiv Detail & Related papers (2023-11-12T16:19:03Z) - Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
We rigorously analyze various generative language models on two dialog tutoring datasets for language learning.
We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios.
Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
arXiv Detail & Related papers (2023-01-24T11:00:17Z) - Human-AI Interaction Design in Machine Teaching [1.5791732557395555]
The paper builds upon previous work where we proposed an MT framework with three components, viz., the teaching interface, the machine learner, and the knowledge base, and focus on the human-AI interaction design involved in realizing the teaching interface.
We outline design decisions that need to be addressed in developing an MT system beginning from an ML task.
arXiv Detail & Related papers (2022-06-10T15:20:05Z) - Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher.
We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
arXiv Detail & Related papers (2022-03-19T03:22:57Z) - Improving mathematical questioning in teacher training [1.794107419334178]
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies.
This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills.
arXiv Detail & Related papers (2021-12-02T05:33:03Z) - Iterative Teacher-Aware Learning [136.05341445369265]
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency.
We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
arXiv Detail & Related papers (2021-10-01T00:27:47Z) - Interactive Teaching for Conversational AI [2.5259192787433706]
Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions.
Motivated by how children learn their first language interacting with adults, this paper describes a new Teachable AI system.
It is capable of learning new language nuggets called concepts, directly from end users using live interactive teaching sessions.
arXiv Detail & Related papers (2020-12-02T04:08:49Z) - Learning Adaptive Language Interfaces through Decomposition [89.21937539950966]
We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition.
Users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps.
arXiv Detail & Related papers (2020-10-11T08:27:07Z) - Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms [50.19997675066203]
We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.
By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
arXiv Detail & Related papers (2020-05-16T02:17:04Z) - Explainable Active Learning (XAL): An Empirical Study of How Local
Explanations Impact Annotator Experience [76.9910678786031]
We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting.
Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
arXiv Detail & Related papers (2020-01-24T22:52:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.