@inproceedings{taniguchi-etal-2024-learning,
title = "Learning Adverbs with Spectral Mixture Kernels",
author = "Taniguchi, Tomoe and
Mochihashi, Daichi and
Kobayashi, Ichiro",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.461",
doi = "10.18653/v1/2024.findings-acl.461",
pages = "7742--7752",
abstract = "For humans and robots to collaborate more in the real world, robots need to understand human intentions from the different manner of their behaviors. In our study, we focus on the meaning of adverbs which describe human motions. We propose a topic model, Hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlet Allocation, which concurrently learns the relationship between those human motions and those adverbs by capturing the frequency kernels that represent motion characteristics and the shared topics of adverbs that depict such motions. We trained the model on datasets we made from movies about {``}walking{''} and {``}dancing{''}, and found that our model outperforms representative neural network models in terms of perplexity score. We also demonstrate our model{'}s ability to determine the adverbs for a given motion and confirmed that the model predicts more appropriate adverbs.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="taniguchi-etal-2024-learning">
<titleInfo>
<title>Learning Adverbs with Spectral Mixture Kernels</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tomoe</namePart>
<namePart type="family">Taniguchi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daichi</namePart>
<namePart type="family">Mochihashi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ichiro</namePart>
<namePart type="family">Kobayashi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lun-Wei</namePart>
<namePart type="family">Ku</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andre</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Srikumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>For humans and robots to collaborate more in the real world, robots need to understand human intentions from the different manner of their behaviors. In our study, we focus on the meaning of adverbs which describe human motions. We propose a topic model, Hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlet Allocation, which concurrently learns the relationship between those human motions and those adverbs by capturing the frequency kernels that represent motion characteristics and the shared topics of adverbs that depict such motions. We trained the model on datasets we made from movies about “walking” and “dancing”, and found that our model outperforms representative neural network models in terms of perplexity score. We also demonstrate our model’s ability to determine the adverbs for a given motion and confirmed that the model predicts more appropriate adverbs.</abstract>
<identifier type="citekey">taniguchi-etal-2024-learning</identifier>
<identifier type="doi">10.18653/v1/2024.findings-acl.461</identifier>
<location>
<url>https://aclanthology.org/2024.findings-acl.461</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>7742</start>
<end>7752</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Learning Adverbs with Spectral Mixture Kernels
%A Taniguchi, Tomoe
%A Mochihashi, Daichi
%A Kobayashi, Ichiro
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F taniguchi-etal-2024-learning
%X For humans and robots to collaborate more in the real world, robots need to understand human intentions from the different manner of their behaviors. In our study, we focus on the meaning of adverbs which describe human motions. We propose a topic model, Hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlet Allocation, which concurrently learns the relationship between those human motions and those adverbs by capturing the frequency kernels that represent motion characteristics and the shared topics of adverbs that depict such motions. We trained the model on datasets we made from movies about “walking” and “dancing”, and found that our model outperforms representative neural network models in terms of perplexity score. We also demonstrate our model’s ability to determine the adverbs for a given motion and confirmed that the model predicts more appropriate adverbs.
%R 10.18653/v1/2024.findings-acl.461
%U https://aclanthology.org/2024.findings-acl.461
%U https://doi.org/10.18653/v1/2024.findings-acl.461
%P 7742-7752
Markdown (Informal)
[Learning Adverbs with Spectral Mixture Kernels](https://aclanthology.org/2024.findings-acl.461) (Taniguchi et al., Findings 2024)
ACL
- Tomoe Taniguchi, Daichi Mochihashi, and Ichiro Kobayashi. 2024. Learning Adverbs with Spectral Mixture Kernels. In Findings of the Association for Computational Linguistics: ACL 2024, pages 7742–7752, Bangkok, Thailand. Association for Computational Linguistics.