Can we learn dialog structure from existing dialogs without ontology or domain assumptions. Understanding dialog structures from existing task oriented human human dialogs can help us automate these dialogues in a better way. Traditionally dialog structures have been created using ontologies that are created by domain experts. However, in our experience getting the ontology right is difficult and time consuming. Like other such tasks an unsupervised approach may do better than hand crafted rules. We propose an unsupervised dialog structure discovery approach that is based on SCAN (Semantic Clustering using Nearest Neighbors). Our approach comprises of two steps, the first being creating clusters of utterances and the second being creation of a structure using inter-cluster transition probabilities. Our main contribution in this paper is the adaptation of SCAN on text data. Unlike the SCAN approach for images, for text we did not train a separate pretext model and were able to use BERT for the same. Similarly for neigbor discovery, instead of augmentation we were able to leverage data variety. Evaluation metrics on dialog structures are a bit subjective, so we have used statistical measures as proxies for structure quality. We have also included our results on an internal human human task oriented 100k dialog dataset. We think SCAN like approaches are very promising for problems that use embedding similarities and should be further explored.
| Published in | Engineering and Applied Sciences (Volume 6, Issue 5) |
| DOI | 10.11648/j.eas.20210605.11 |
| Page(s) | 82-85 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Unsupervised Learning, Dialog Structure Discovery, Text Classification, Clustering
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APA Style
Apurba Nath, Aayush Kubba. (2021). Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data. Engineering and Applied Sciences, 6(5), 82-85. https://doi.org/10.11648/j.eas.20210605.11
ACS Style
Apurba Nath; Aayush Kubba. Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data. Eng. Appl. Sci. 2021, 6(5), 82-85. doi: 10.11648/j.eas.20210605.11
AMA Style
Apurba Nath, Aayush Kubba. Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data. Eng Appl Sci. 2021;6(5):82-85. doi: 10.11648/j.eas.20210605.11
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Download@article{10.11648/j.eas.20210605.11,
author = {Apurba Nath and Aayush Kubba},
title = {Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data},
journal = {Engineering and Applied Sciences},
volume = {6},
number = {5},
pages = {82-85},
doi = {10.11648/j.eas.20210605.11},
url = {https://doi.org/10.11648/j.eas.20210605.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20210605.11},
abstract = {Can we learn dialog structure from existing dialogs without ontology or domain assumptions. Understanding dialog structures from existing task oriented human human dialogs can help us automate these dialogues in a better way. Traditionally dialog structures have been created using ontologies that are created by domain experts. However, in our experience getting the ontology right is difficult and time consuming. Like other such tasks an unsupervised approach may do better than hand crafted rules. We propose an unsupervised dialog structure discovery approach that is based on SCAN (Semantic Clustering using Nearest Neighbors). Our approach comprises of two steps, the first being creating clusters of utterances and the second being creation of a structure using inter-cluster transition probabilities. Our main contribution in this paper is the adaptation of SCAN on text data. Unlike the SCAN approach for images, for text we did not train a separate pretext model and were able to use BERT for the same. Similarly for neigbor discovery, instead of augmentation we were able to leverage data variety. Evaluation metrics on dialog structures are a bit subjective, so we have used statistical measures as proxies for structure quality. We have also included our results on an internal human human task oriented 100k dialog dataset. We think SCAN like approaches are very promising for problems that use embedding similarities and should be further explored.},
year = {2021}
}
TY - JOUR T1 - Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data AU - Apurba Nath AU - Aayush Kubba Y1 - 2021/09/07 PY - 2021 N1 - https://doi.org/10.11648/j.eas.20210605.11 DO - 10.11648/j.eas.20210605.11 T2 - Engineering and Applied Sciences JF - Engineering and Applied Sciences JO - Engineering and Applied Sciences SP - 82 EP - 85 PB - Science Publishing Group SN - 2575-1468 UR - https://doi.org/10.11648/j.eas.20210605.11 AB - Can we learn dialog structure from existing dialogs without ontology or domain assumptions. Understanding dialog structures from existing task oriented human human dialogs can help us automate these dialogues in a better way. Traditionally dialog structures have been created using ontologies that are created by domain experts. However, in our experience getting the ontology right is difficult and time consuming. Like other such tasks an unsupervised approach may do better than hand crafted rules. We propose an unsupervised dialog structure discovery approach that is based on SCAN (Semantic Clustering using Nearest Neighbors). Our approach comprises of two steps, the first being creating clusters of utterances and the second being creation of a structure using inter-cluster transition probabilities. Our main contribution in this paper is the adaptation of SCAN on text data. Unlike the SCAN approach for images, for text we did not train a separate pretext model and were able to use BERT for the same. Similarly for neigbor discovery, instead of augmentation we were able to leverage data variety. Evaluation metrics on dialog structures are a bit subjective, so we have used statistical measures as proxies for structure quality. We have also included our results on an internal human human task oriented 100k dialog dataset. We think SCAN like approaches are very promising for problems that use embedding similarities and should be further explored. VL - 6 IS - 5 ER -
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