@inproceedings{mannan-krishnaswamy-2022-go,
title = "Where Am {I} and Where Should {I} Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task",
author = "Mannan, Sheikh and
Krishnaswamy, Nikhil",
editor = "Dobnik, Simon and
Grove, Julian and
Sayeed, Asad",
booktitle = "Proceedings of the 2022 CLASP Conference on (Dis)embodiment",
month = sep,
year = "2022",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clasp-1.8/",
pages = "70--79",
abstract = "In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects' joystick motions as well as the subjects' proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mannan-krishnaswamy-2022-go">
<titleInfo>
<title>Where Am I and Where Should I Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sheikh</namePart>
<namePart type="family">Mannan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikhil</namePart>
<namePart type="family">Krishnaswamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2022 CLASP Conference on (Dis)embodiment</title>
</titleInfo>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Dobnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julian</namePart>
<namePart type="family">Grove</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asad</namePart>
<namePart type="family">Sayeed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gothenburg, Sweden</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects’ joystick motions as well as the subjects’ proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans.</abstract>
<identifier type="citekey">mannan-krishnaswamy-2022-go</identifier>
<location>
<url>https://aclanthology.org/2022.clasp-1.8/</url>
</location>
<part>
<date>2022-09</date>
<extent unit="page">
<start>70</start>
<end>79</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Where Am I and Where Should I Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task
%A Mannan, Sheikh
%A Krishnaswamy, Nikhil
%Y Dobnik, Simon
%Y Grove, Julian
%Y Sayeed, Asad
%S Proceedings of the 2022 CLASP Conference on (Dis)embodiment
%D 2022
%8 September
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F mannan-krishnaswamy-2022-go
%X In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects’ joystick motions as well as the subjects’ proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans.
%U https://aclanthology.org/2022.clasp-1.8/
%P 70-79
Markdown (Informal)
[Where Am I and Where Should I Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task](https://aclanthology.org/2022.clasp-1.8/) (Mannan & Krishnaswamy, CLASP 2022)
ACL