The Experts below are selected from a list of 202521 Experts worldwide ranked by ideXlab platform
Alexander G Gray - One of the best experts on this subject based on the ideXlab platform.
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loa logical optimal actions for text based Interaction games
arXiv: Artificial Intelligence, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Code: https://github.com/ibm/loa
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loa logical optimal actions for text based Interaction games
Meeting of the Association for Computational Linguistics, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa
Daiki Kimura - One of the best experts on this subject based on the ideXlab platform.
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loa logical optimal actions for text based Interaction games
arXiv: Artificial Intelligence, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Code: https://github.com/ibm/loa
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loa logical optimal actions for text based Interaction games
Meeting of the Association for Computational Linguistics, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa
Ahmed Hassan Awadallah - One of the best experts on this subject based on the ideXlab platform.
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nl edit correcting semantic parse errors through natural Language Interaction
North American Chapter of the Association for Computational Linguistics, 2021Co-Authors: Ahmed Elgohary, Christopher A Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan AwadallahAbstract:We study semantic parsing in an interactive setting in which users correct errors with natural Language feedback. We present NL-EDIT, a model for interpreting natural Language feedback in the Interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.
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nl edit correcting semantic parse errors through natural Language Interaction
arXiv: Computation and Language, 2021Co-Authors: Ahmed Elgohary, Christopher A Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan AwadallahAbstract:We study semantic parsing in an interactive setting in which users correct errors with natural Language feedback. We present NL-EDIT, a model for interpreting natural Language feedback in the Interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at this http URL.
Arne Nagels - One of the best experts on this subject based on the ideXlab platform.
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gesture s body orientation modulates the n400 for visual sentences primed by gestures
Human Brain Mapping, 2020Co-Authors: Svenja Luell, R Muralikrishnan, Benjamin Straube, Arne NagelsAbstract:Body orientation of gesture entails social-communicative intention, and may thus influence how gestures are perceived and comprehended together with auditory speech during face-to-face communication. To date, despite the emergence of neuroscientific literature on the role of body orientation on hand action perception, limited studies have directly investigated the role of body orientation in the Interaction between gesture and Language. To address this research question, we carried out an electroencephalography (EEG) experiment presenting to participants (n = 21) videos of frontal and lateral communicative hand gestures of 5 s (e.g., raising a hand), followed by visually presented sentences that are either congruent or incongruent with the gesture (e.g., "the mountain is high/low…"). Participants underwent a semantic probe task, judging whether a target word is related or unrelated to the gesture-sentence event. EEG results suggest that, during the perception phase of handgestures, while both frontal and lateral gestures elicited a power decrease in both the alpha (8-12 Hz) and the beta (16-24 Hz) bands, lateral versus frontal gestures elicited reduced power decrease in the beta band, source-located to the medial prefrontal cortex. For sentence comprehension, at the critical word whose meaning is congruent/incongruent with the gesture prime, frontal gestures elicited an N400 effect for gesture-sentence incongruency. More importantly, this incongruency effect was significantly reduced for lateral gestures. These findings suggest that body orientation plays an important role in gesture perception, and that its inferred social-communicative intention may influence gesture-Language Interaction at semantic level.
Don Joven Agravante - One of the best experts on this subject based on the ideXlab platform.
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loa logical optimal actions for text based Interaction games
arXiv: Artificial Intelligence, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Code: https://github.com/ibm/loa
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loa logical optimal actions for text based Interaction games
Meeting of the Association for Computational Linguistics, 2021Co-Authors: Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander G GrayAbstract:We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural Language Interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa
