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Maximizing the effect of visual feedback for pronunciation instruction: A comparative analysis of three approaches
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In: School of Languages and Cultures Faculty Publications (2021)
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| 82 |
La retroacció correctiva oral amb estudiants adults poc escolaritzats
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| 83 |
Eficacia de los procedimientos de retroalimentación para mejorar el conocimiento declarativo de los estudiantes en un entorno de lectura orientado a tareas
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| 84 |
Rediscovering feedback and experiential learning in the English-medium instruction classroom
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In: Journal of University Teaching & Learning Practice (2021)
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| 85 |
The development and testing of an online scenario-based learning activity to prepare preservice teachers for teaching placements
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In: Test Series for Scopus Harvesting 2021 (2021)
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| 86 |
Preservice teachers’ perceptions of feedback: The importance of timing, purpose, and delivery
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In: Journal of University Teaching & Learning Practice (2021)
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| 87 |
DIY assessment feedback: Building engagement, trust and transparency in the feedback process
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In: Journal of University Teaching & Learning Practice (2021)
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| 88 |
Visual-Performance Feedback on Acknowledgement Within a Positive Behavior Intervention and Support System
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| 90 |
Supervised attention from natural language feedback for reinforcement learning
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Abstract:
In this paper, we introduce a new approach to Reinforcement Learning (RL) called “supervised attention” from human feedback which focuses on novel task learning from human interaction on relevant features of the environment, which we hypothesize will allow for effective learning from limited training data. We wanted to answer the following question: does the addition of language to existing RL frameworks improve agent learning? We wanted to show that language helps the agent pick out the most important features in its perception. We tested many methods for implementing this concept and settled on incorporating language feedback via a template matching scheme. While more sophisticated techniques, such as attention, would be better at grounding the language, we discovered this task is non trivial for our choice of environment. Using deep learning methods, we translate human linguistic narration to a saliency map over the perceptual field. This saliency map is used to inform a deep-reinforcement learning system which features in the visual observation are most important relative to its position in the environment and optimize task learning. We establish a baseline model using deep TAMER and test our framework on Montezuma’s Revenge, the most difficult game in the Atari Arcade suite. However, our final framework demonstrates the incompatibility of language in the Atari suite in a supervised attention setting. The ultimate result showed that as long as the agent’s position in the observation was clear, the model ignores surrounding contextual information, regardless of potential benefit. We conclude that the Atari network of games is unsuitable for grounding natural language in the high dimensional state spaces. Further development of sophisticated simulations is required. ; Computer Sciences
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Keyword:
Learning from feedback; Natural language feedback; Reinforcement learning; Supervised attention
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URL: https://hdl.handle.net/2152/87497
https://doi.org/10.26153/tsw/14441
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| 91 |
Multimodality and translanguaging in negotiation of meaning
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| 92 |
Self-reported feedback in ICT-delivered aphasia rehabilitation: a literature review
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| 93 |
Feedback and Creativity in Interior Design Studio: A Case study-mixed methods of a Junior Level Light Fixture Project
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| 94 |
Critical Language Awareness in the Multilingual Writing Classroom: A Self-Study of Teacher Feedback Practices
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In: Doctoral Dissertations (2021)
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| 95 |
Feedback is a gift: Do Video-enhanced rubrics result in providing better peer feedback than textual rubrics?
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In: Practical Assessment, Research, and Evaluation (2021)
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| 96 |
EFL Lecturers' Perception and Practice of Screencast Feedback
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In: JEELS (Journal of English Education and Linguistics Studies), Vol 8, Iss 1, Pp 1-25 (2021) (2021)
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| 97 |
Focused direct corrective feedback: Effects on the elementary English learners’ written syntactic complexity
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In: Eurasian Journal of Applied Linguistics, Vol 7, Iss 1, Pp 132-150 (2021) (2021)
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| 98 |
Online and face-to-face peer review in academic writing: Frequency and preferences
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In: Eurasian Journal of Applied Linguistics, Vol 7, Iss 1, Pp 169-201 (2021) (2021)
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| 99 |
L2 writers’ perspectives on face-to-face and anonymous peer review: Voices from China
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In: Porta Linguarum: revista internacional de didáctica de las lenguas extranjeras, ISSN 1697-7467, Nº. 35, 2021, pags. 149-164 (2021)
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| 100 |
Effects of recasts, clarification requests on suprasegment development of English intonation
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In: Porta Linguarum: revista internacional de didáctica de las lenguas extranjeras, ISSN 1697-7467, Nº. 35, 2021, pags. 311-325 (2021)
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