Dialogue Term Extraction using Transfer Learning and Topological Data Analysis

Renato Vukovic, Michael Heck, Benjamin Ruppik, Carel van Niekerk, Marcus Zibrowius, Milica Gasic


Abstract
Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots and values. As we move towards adaptable dialogue systems where knowledge about domains, slots and values may change, there is an increasing need to automatically extract these terms from raw dialogues or related non-dialogue data on a large scale. In this paper, we take an important step in this direction by exploring different features that can enable systems to discover realisations of domains, slots and values in dialogues in a purely data-driven fashion. The features that we examine stem from word embeddings, language modelling features, as well as topological features of the word embedding space. To examine the utility of each feature set, we train a seed model based on the widely used MultiWOZ data-set. Then, we apply this model to a different corpus, the Schema-guided dialogue data-set. Our method outperforms the previously proposed approach that relies solely on word embeddings. We also demonstrate that each of the features is responsible for discovering different kinds of content. We believe our results warrant further research towards ontology induction, and continued harnessing of topological data analysis for dialogue and natural language processing research.
Anthology ID:
2022.sigdial-1.53
Volume:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2022
Address:
Edinburgh, UK
Editors:
Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
564–581
Language:
URL:
https://aclanthology.org/2022.sigdial-1.53
DOI:
10.18653/v1/2022.sigdial-1.53
Bibkey:
Cite (ACL):
Renato Vukovic, Michael Heck, Benjamin Ruppik, Carel van Niekerk, Marcus Zibrowius, and Milica Gasic. 2022. Dialogue Term Extraction using Transfer Learning and Topological Data Analysis. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 564–581, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis (Vukovic et al., SIGDIAL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.sigdial-1.53.pdf
Video:
 https://youtu.be/keSRDRwRK3Y
Data
MultiWOZSGD