DGC-CRL: Dependency Graph Convolution based Contrastive Representation Learning for Chinese Medical Question Matching Article Swipe
W. Chen
,
Quzhen Baima
,
Qi Zhao
,
Binhui Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2023.08.170
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2023.08.170
As one kind of domain-specific question answering (QA) systems, the medical QA systems require much more stability, fast system speed and response accuracy. Therefore, the retrieval based QA systems are more suitable, among which the deep semantic matching models become prevalent to be studied and they are playing the very important role on the quality of retrieval based medical QA systems. In this paper, we propose a two-stage solution (named with Dependency Graph Convolution based Contrastive Representation Learning) which includes a dependency graph convolution module to explicitly capture the semantic similarity between Chinese questions. At the first stage, we adopt the contrastive learning to further distinguish the similarity within the domain-specific corpus itself and learn discriminative textual representations. At the second stage, the down-streaming question matching task is benefited by using the newly-learned representations. In our experiments, we collect two Chinese medical datasets (CBLUE-STS and COVID-19) and the results can demonstrate that our proposed method is effective and general to different medical QA corpora. Also the ablation experiments indicate the proposed Dependency Graph Convolution module and contrastive learning method are both efficient.
Related Topics
Concepts
Computer science
Artificial intelligence
Discriminative model
Natural language processing
Graph
Semantic matching
Matching (statistics)
Convolution (computer science)
Dependency (UML)
Feature learning
Dependency graph
Convolutional neural network
Theoretical computer science
Artificial neural network
Mathematics
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2023.08.170
- OA Status
- diamond
- References
- 50
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386336870Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2023.08.170Digital Object Identifier
- Title
-
DGC-CRL: Dependency Graph Convolution based Contrastive Representation Learning for Chinese Medical Question MatchingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
W. Chen, Quzhen Baima, Qi Zhao, Binhui WangList of authors in order
- Landing page
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https://doi.org/10.1016/j.procs.2023.08.170Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2023.08.170Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Discriminative model, Natural language processing, Graph, Semantic matching, Matching (statistics), Convolution (computer science), Dependency (UML), Feature learning, Dependency graph, Convolutional neural network, Theoretical computer science, Artificial neural network, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 41, 85, 103, 159 |
| abstract_inverted_index.we | 64, 98, 137 |
| abstract_inverted_index.and | 20, 44, 113, 144, 146, 157, 175 |
| abstract_inverted_index.are | 29, 46, 179 |
| abstract_inverted_index.can | 149 |
| abstract_inverted_index.one | 1 |
| abstract_inverted_index.our | 135, 152 |
| abstract_inverted_index.the | 9, 24, 34, 48, 53, 88, 95, 100, 106, 109, 119, 122, 131, 147, 165, 169 |
| abstract_inverted_index.two | 139 |
| abstract_inverted_index.(QA) | 7 |
| abstract_inverted_index.Also | 164 |
| abstract_inverted_index.both | 180 |
| abstract_inverted_index.deep | 35 |
| abstract_inverted_index.fast | 17 |
| abstract_inverted_index.kind | 2 |
| abstract_inverted_index.more | 15, 30 |
| abstract_inverted_index.much | 14 |
| abstract_inverted_index.role | 51 |
| abstract_inverted_index.task | 126 |
| abstract_inverted_index.that | 151 |
| abstract_inverted_index.they | 45 |
| abstract_inverted_index.this | 62 |
| abstract_inverted_index.very | 49 |
| abstract_inverted_index.with | 70 |
| abstract_inverted_index.Graph | 72, 172 |
| abstract_inverted_index.adopt | 99 |
| abstract_inverted_index.among | 32 |
| abstract_inverted_index.based | 26, 57, 74 |
| abstract_inverted_index.first | 96 |
| abstract_inverted_index.graph | 82 |
| abstract_inverted_index.learn | 114 |
| abstract_inverted_index.speed | 19 |
| abstract_inverted_index.using | 130 |
| abstract_inverted_index.which | 33, 78 |
| abstract_inverted_index.(named | 69 |
| abstract_inverted_index.become | 39 |
| abstract_inverted_index.corpus | 111 |
| abstract_inverted_index.itself | 112 |
| abstract_inverted_index.method | 154, 178 |
| abstract_inverted_index.models | 38 |
| abstract_inverted_index.module | 84, 174 |
| abstract_inverted_index.paper, | 63 |
| abstract_inverted_index.second | 120 |
| abstract_inverted_index.stage, | 97, 121 |
| abstract_inverted_index.system | 18 |
| abstract_inverted_index.within | 108 |
| abstract_inverted_index.Chinese | 92, 140 |
| abstract_inverted_index.between | 91 |
| abstract_inverted_index.capture | 87 |
| abstract_inverted_index.collect | 138 |
| abstract_inverted_index.further | 104 |
| abstract_inverted_index.general | 158 |
| abstract_inverted_index.medical | 10, 58, 141, 161 |
| abstract_inverted_index.playing | 47 |
| abstract_inverted_index.propose | 65 |
| abstract_inverted_index.quality | 54 |
| abstract_inverted_index.require | 13 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.studied | 43 |
| abstract_inverted_index.systems | 12, 28 |
| abstract_inverted_index.textual | 116 |
| abstract_inverted_index.ablation | 166 |
| abstract_inverted_index.corpora. | 163 |
| abstract_inverted_index.datasets | 142 |
| abstract_inverted_index.includes | 79 |
| abstract_inverted_index.indicate | 168 |
| abstract_inverted_index.learning | 102, 177 |
| abstract_inverted_index.matching | 37, 125 |
| abstract_inverted_index.proposed | 153, 170 |
| abstract_inverted_index.question | 5, 124 |
| abstract_inverted_index.response | 21 |
| abstract_inverted_index.semantic | 36, 89 |
| abstract_inverted_index.solution | 68 |
| abstract_inverted_index.systems, | 8 |
| abstract_inverted_index.systems. | 60 |
| abstract_inverted_index.COVID-19) | 145 |
| abstract_inverted_index.Learning) | 77 |
| abstract_inverted_index.accuracy. | 22 |
| abstract_inverted_index.answering | 6 |
| abstract_inverted_index.benefited | 128 |
| abstract_inverted_index.different | 160 |
| abstract_inverted_index.effective | 156 |
| abstract_inverted_index.important | 50 |
| abstract_inverted_index.prevalent | 40 |
| abstract_inverted_index.retrieval | 25, 56 |
| abstract_inverted_index.suitable, | 31 |
| abstract_inverted_index.two-stage | 67 |
| abstract_inverted_index.(CBLUE-STS | 143 |
| abstract_inverted_index.Dependency | 71, 171 |
| abstract_inverted_index.Therefore, | 23 |
| abstract_inverted_index.dependency | 81 |
| abstract_inverted_index.efficient. | 181 |
| abstract_inverted_index.explicitly | 86 |
| abstract_inverted_index.questions. | 93 |
| abstract_inverted_index.similarity | 90, 107 |
| abstract_inverted_index.stability, | 16 |
| abstract_inverted_index.Contrastive | 75 |
| abstract_inverted_index.Convolution | 73, 173 |
| abstract_inverted_index.contrastive | 101, 176 |
| abstract_inverted_index.convolution | 83 |
| abstract_inverted_index.demonstrate | 150 |
| abstract_inverted_index.distinguish | 105 |
| abstract_inverted_index.experiments | 167 |
| abstract_inverted_index.experiments, | 136 |
| abstract_inverted_index.newly-learned | 132 |
| abstract_inverted_index.Representation | 76 |
| abstract_inverted_index.discriminative | 115 |
| abstract_inverted_index.down-streaming | 123 |
| abstract_inverted_index.domain-specific | 4, 110 |
| abstract_inverted_index.representations. | 117, 133 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5024881953 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I205237279 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.12470474 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |