Dialogue Term Extraction using Transfer Learning and Topological Data Analysis Article Swipe
Renato Vukovic
,
Michael Heck
,
Benjamin Ruppik
,
Carel van Niekerk
,
Marcus Zibrowius
,
Milica Gašić
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.10448
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.10448
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 realizations 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.10448
- https://arxiv.org/pdf/2208.10448
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292945961
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4292945961Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2208.10448Digital Object Identifier
- Title
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Dialogue Term Extraction using Transfer Learning and Topological Data AnalysisWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-22Full publication date if available
- Authors
-
Renato Vukovic, Michael Heck, Benjamin Ruppik, Carel van Niekerk, Marcus Zibrowius, Milica GašićList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.10448Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2208.10448Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2208.10448Direct OA link when available
- Concepts
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Computer science, Schema (genetic algorithms), Natural language processing, Set (abstract data type), Word (group theory), Term (time), Embedding, Artificial intelligence, Natural language, Information retrieval, Linguistics, Physics, Philosophy, Programming language, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.where | 37 |
| abstract_inverted_index.about, | 22 |
| abstract_inverted_index.enable | 84 |
| abstract_inverted_index.method | 157 |
| abstract_inverted_index.paper, | 69 |
| abstract_inverted_index.purely | 98 |
| abstract_inverted_index.relies | 164 |
| abstract_inverted_index.scale. | 66 |
| abstract_inverted_index.slots, | 27, 41, 91 |
| abstract_inverted_index.solely | 165 |
| abstract_inverted_index.space. | 122 |
| abstract_inverted_index.values | 43, 93 |
| abstract_inverted_index.widely | 139 |
| abstract_inverted_index.believe | 186 |
| abstract_inverted_index.change, | 45 |
| abstract_inverted_index.corpus, | 151 |
| abstract_inverted_index.domain, | 26 |
| abstract_inverted_index.examine | 105, 124 |
| abstract_inverted_index.extract | 53 |
| abstract_inverted_index.feature | 129 |
| abstract_inverted_index.further | 23, 190 |
| abstract_inverted_index.inquire | 21 |
| abstract_inverted_index.natural | 9, 205 |
| abstract_inverted_index.related | 60 |
| abstract_inverted_index.results | 188 |
| abstract_inverted_index.systems | 3, 36, 85 |
| abstract_inverted_index.towards | 33, 192 |
| abstract_inverted_index.utility | 126 |
| abstract_inverted_index.values. | 29 |
| abstract_inverted_index.warrant | 189 |
| abstract_inverted_index.Dialogue | 154 |
| abstract_inverted_index.MultiWOZ | 141 |
| abstract_inverted_index.analysis | 201 |
| abstract_inverted_index.approach | 162 |
| abstract_inverted_index.content. | 184 |
| abstract_inverted_index.data-set | 15 |
| abstract_inverted_index.designed | 6 |
| abstract_inverted_index.dialogue | 2, 35, 203 |
| abstract_inverted_index.discover | 87 |
| abstract_inverted_index.domains, | 40, 90 |
| abstract_inverted_index.entities | 17 |
| abstract_inverted_index.fashion. | 100 |
| abstract_inverted_index.features | 81, 102, 117, 176 |
| abstract_inverted_index.language | 10, 110, 206 |
| abstract_inverted_index.ontology | 193 |
| abstract_inverted_index.oriented | 1 |
| abstract_inverted_index.proposed | 161 |
| abstract_inverted_index.research | 191 |
| abstract_inverted_index.adaptable | 34 |
| abstract_inverted_index.continued | 196 |
| abstract_inverted_index.data-set. | 142, 155 |
| abstract_inverted_index.described | 24 |
| abstract_inverted_index.dialogues | 58, 95 |
| abstract_inverted_index.different | 80, 150, 181 |
| abstract_inverted_index.direction | 77 |
| abstract_inverted_index.embedding | 121 |
| abstract_inverted_index.exploring | 79 |
| abstract_inverted_index.features, | 112 |
| abstract_inverted_index.important | 73 |
| abstract_inverted_index.interface | 11 |
| abstract_inverted_index.knowledge | 38 |
| abstract_inverted_index.modelling | 111 |
| abstract_inverted_index.research. | 208 |
| abstract_inverted_index.harnessing | 197 |
| abstract_inverted_index.increasing | 49 |
| abstract_inverted_index.induction, | 194 |
| abstract_inverted_index.originally | 5 |
| abstract_inverted_index.previously | 160 |
| abstract_inverted_index.processing | 207 |
| abstract_inverted_index.data-driven | 99 |
| abstract_inverted_index.demonstrate | 171 |
| abstract_inverted_index.discovering | 180 |
| abstract_inverted_index.embeddings, | 109 |
| abstract_inverted_index.embeddings. | 168 |
| abstract_inverted_index.outperforms | 158 |
| abstract_inverted_index.responsible | 178 |
| abstract_inverted_index.topological | 116, 199 |
| abstract_inverted_index.non-dialogue | 61 |
| abstract_inverted_index.realizations | 88 |
| abstract_inverted_index.Schema-Guided | 153 |
| abstract_inverted_index.automatically | 52 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.50681094 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |