https://github.com/LeonCrashCode/DRS-Cross-Lingual-Training
공식 코드MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Discourse Representation Structures (DRS) are formal meaning representations introduced by Discourse Representation Theory. DRS parsing is a complex task, comprising other NLP tasks, such as semantic role labeling, word sense disambiguation, co-reference resolution and named entity tagging. Also, DRSs show explicit scope for certain operators, which allows for a more principled and linguistically motivated treatment of negation, modals and quantification, as has been advocated in formal semantics. Moreover, DRSs can be translated to formal logic, which allows for automatic forms of inference by third parties.
Description from NLP Progress
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing
Exploring Neural Methods for Parsing Discourse Representation Structures
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.