Thesis
Scalable syntactic inductive biases for neural language models
- Abstract:
-
Natural language has a sequential surface form, although its underlying structure has been argued to be hierarchical and tree-structured in nature, whereby smaller linguistic units like words are recursively composed to form larger ones, such as phrases and sentences. This thesis aims to answer the following open research questions: To what extent---if at all---can more explicit notions of hierarchical syntactic structures further improve the performance of neural models within NL...
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Authors
Contributors
+ Blunsom, P
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Computer Science
- Role:
- Supervisor
- ORCID:
- 0000-0003-4558-2457
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Funding agency for:
- Kuncoro, AS
- Programme:
- EPSRC Doctoral Training Partnership Studentship
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
-
English
- Keywords:
- Subjects:
- Deposit date:
-
2024-04-29
Terms of use
- Copyright holder:
- Kuncoro, AS
- Copyright date:
- 2022
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