What does KnowBert-UMLS forget? Article Swipe
Guilhem Piat
,
Nasredine Semmar
,
Julien Tourille
,
Alexandre Allauzen
,
Hassane Essafi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/aiccsa59173.2023.10479333
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/aiccsa59173.2023.10479333
Integrating a source of structured prior knowledge, such as a knowledge graph, into transformer-based language models is an increasingly popular method for increasing data efficiency and adapting them to a target domain. However, most methods for integrating structured knowledge into language models require additional training in order to adapt the model to the non-textual modality. This process typically leads to some amount of catastrophic forgetting on the general domain. KnowBert is one such knowledge integration method which can incorporate information from a variety of knowledge graphs to enhance the capabilities of transformer-based language models such as BERT. We conduct a qualitative analysis of the results of KnowBert-UMLS, a biomedically specialized KnowBert model, on a variety of linguistic tasks. Our results reveal that its increased understanding of biomedical concepts comes at the cost, specifically, of general common-sense knowledge and understanding of casual speech.
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- article
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- https://doi.org/10.1109/aiccsa59173.2023.10479333
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All OpenAlex metadata
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https://doi.org/10.1109/aiccsa59173.2023.10479333Digital Object Identifier
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What does KnowBert-UMLS forget?Work title
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articleOpenAlex work type
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2023Year of publication
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2023-12-04Full publication date if available
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Guilhem Piat, Nasredine Semmar, Julien Tourille, Alexandre Allauzen, Hassane EssafiList of authors in order
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https://doi.org/10.1109/aiccsa59173.2023.10479333Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://cea.hal.science/cea-04559677Direct OA link when available
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Unified Medical Language System, Computer science, Natural language processing, Information retrieval, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.casual | 140 |
| abstract_inverted_index.graph, | 11 |
| abstract_inverted_index.graphs | 85 |
| abstract_inverted_index.method | 20, 75 |
| abstract_inverted_index.model, | 111 |
| abstract_inverted_index.models | 15, 41, 93 |
| abstract_inverted_index.reveal | 120 |
| abstract_inverted_index.source | 2 |
| abstract_inverted_index.target | 30 |
| abstract_inverted_index.tasks. | 117 |
| abstract_inverted_index.conduct | 98 |
| abstract_inverted_index.domain. | 31, 68 |
| abstract_inverted_index.enhance | 87 |
| abstract_inverted_index.general | 67, 134 |
| abstract_inverted_index.methods | 34 |
| abstract_inverted_index.popular | 19 |
| abstract_inverted_index.process | 56 |
| abstract_inverted_index.require | 42 |
| abstract_inverted_index.results | 104, 119 |
| abstract_inverted_index.speech. | 141 |
| abstract_inverted_index.variety | 82, 114 |
| abstract_inverted_index.However, | 32 |
| abstract_inverted_index.KnowBert | 69, 110 |
| abstract_inverted_index.adapting | 26 |
| abstract_inverted_index.analysis | 101 |
| abstract_inverted_index.concepts | 127 |
| abstract_inverted_index.language | 14, 40, 92 |
| abstract_inverted_index.training | 44 |
| abstract_inverted_index.increased | 123 |
| abstract_inverted_index.knowledge | 10, 38, 73, 84, 136 |
| abstract_inverted_index.modality. | 54 |
| abstract_inverted_index.typically | 57 |
| abstract_inverted_index.additional | 43 |
| abstract_inverted_index.biomedical | 126 |
| abstract_inverted_index.efficiency | 24 |
| abstract_inverted_index.forgetting | 64 |
| abstract_inverted_index.increasing | 22 |
| abstract_inverted_index.knowledge, | 6 |
| abstract_inverted_index.linguistic | 116 |
| abstract_inverted_index.structured | 4, 37 |
| abstract_inverted_index.Integrating | 0 |
| abstract_inverted_index.incorporate | 78 |
| abstract_inverted_index.information | 79 |
| abstract_inverted_index.integrating | 36 |
| abstract_inverted_index.integration | 74 |
| abstract_inverted_index.non-textual | 53 |
| abstract_inverted_index.qualitative | 100 |
| abstract_inverted_index.specialized | 109 |
| abstract_inverted_index.biomedically | 108 |
| abstract_inverted_index.capabilities | 89 |
| abstract_inverted_index.catastrophic | 63 |
| abstract_inverted_index.common-sense | 135 |
| abstract_inverted_index.increasingly | 18 |
| abstract_inverted_index.specifically, | 132 |
| abstract_inverted_index.understanding | 124, 138 |
| abstract_inverted_index.KnowBert-UMLS, | 106 |
| abstract_inverted_index.transformer-based | 13, 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.24911142 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |