An Unsupervised Baseline For Dialogue Breakdown Detection Using Ouf-of-distribution Detection

Name: An Unsupervised Baseline For Dialogue Breakdown Detection Using Ouf-of-distribution Detection
Published: 2023-06-05
License: http://creativecommons.org/publicdomain/zero/1.0

Open this dataset in the live repository

Important usage condition: Use and redistribution are governed by CC0 1.0. Review and comply with the license before using the data.

Persistent identifier: doi:10.60507/FK2/MAVB6H
Published version: 1.0
Publication date: 2023-06-05
License: CC0 1.0

Description

For the last several decades, a focus of artificial intelligence work has been computer-based systems for interacting with humans. Such a system must ensure smooth communication to provide thoughtful answers by interacting with the system. A major critical issue is they can create an unwanted statement that results in a breakdown of dialogue, which degrades the overall interaction quality. Thus, it is incredibly beneficial to employ a system to detect whether a breakdown hampers the conversation flow. In this work, we propose an unsupervised dialogue breakdown detection technique that can be trained using only dialogue data and no other supervision. Our experiments show that using out-of-distribution detection methods can perform on par with competing supervised methods that use labeled English data. On the DBDC4 benchmark, it is the only unsupervised approach to our knowledge.

Creators

Nedelchev, Rostislav

Keywords

dialogue systems, out of distribution detection, dialogue breakdown

Files

Before downloading: Use and redistribution are governed by CC0 1.0. Review and comply with the license before using the data.

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Citation

Nedelchev, Rostislav, 2023-06-05, An Unsupervised Baseline For Dialogue Breakdown Detection Using Ouf-of-distribution Detection, doi:10.60507/FK2/MAVB6H, V1.0

Additional Dataverse fields

Id	2
Dataset Type	dataset
Internal Version Number	11
Latest Version Publishing State	RELEASED
Deaccession Link	Not supplied
Release Time	2023-06-05T08:01:07Z
Create Time	2023-05-20T10:17:32Z
Citation Date	2023-06-05
File Access Request	True

Export metadata

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Complete Dataverse metadata

Citation Metadata

Title title

An Unsupervised Baseline For Dialogue Breakdown Detection Using Ouf-of-distribution Detection

Author author

Author Name
Nedelchev, Rostislav

Dataset Contact datasetContact

Dataset Contact Name
Nedelchev, Rostislav
Dataset Contact Email
rostislav.nedelchev@uni-bonn.de

Ds Description dsDescription

Ds Description Value
For the last several decades, a focus of artificial intelligence work has been computer-based systems for interacting with humans. Such a system must ensure smooth communication to provide thoughtful answers by interacting with the system. A major critical issue is they can create an unwanted statement that results in a breakdown of dialogue, which degrades the overall interaction quality. Thus, it is incredibly beneficial to employ a system to detect whether a breakdown hampers the conversation flow. In this work, we propose an unsupervised dialogue breakdown detection technique that can be trained using only dialogue data and no other supervision. Our experiments show that using out-of-distribution detection methods can perform on par with competing supervised methods that use labeled English data. On the DBDC4 benchmark, it is the only unsupervised approach to our knowledge.

Subject subject

Computer and Information Science

Free Keyword freeKeyword

Free Keyword Value
dialogue systems
Free Keyword Value
out of distribution detection
Free Keyword Value
dialogue breakdown

Publication publication

Publication Citation
Nedelchev, Rostislav: Automatic Evaluation of Dialogue-Systems Using Neural-Network Methods. - Bonn, 2023. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn. Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-70983
Publication IDType
urn
Publication IDNumber
urn:nbn:de:hbz:5-70983
Publication URL
https://nbn-resolving.org/urn:nbn:de:hbz:5-70983

Depositor depositor

Nedelchev, Rostislav

Date Of Deposit dateOfDeposit

2023-05-20

Software software

Software Name
Python
Software Version
3.8
Software Name
virtual environment like (Ana|Mini)conda

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