https://github.com/Nickolas-option/QA_on_Tabular_Data_SemEval2025_Task8
공식 코드Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
Papers with Code 아카이브
Semantic Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 20
- 보존된 평가 기록
- 78
Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.
Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Noahs-ARK/NeurboParser
공식 코드https://github.com/Noahs-ARK/SPIGOT
공식 코드https://github.com/Noahs-ARK/open-sesame
공식 코드Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
https://github.com/OhadRubin/SmBop
공식 코드https://github.com/Oneplus/tamr
공식 코드https://github.com/PaddlePaddle/Research
공식 코드Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
https://github.com/RUCAIBox/Awesome-KBQA
공식 코드https://github.com/RUCKBReasoning/DPO_Text2SQL
공식 코드https://github.com/RUCKBReasoning/OmniSQL
공식 코드OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
https://github.com/Rcrossmeister/Knowledge-to-SQL
공식 코드Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM
https://github.com/RichardHGL/WSDM2021_NSM
공식 코드Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
https://github.com/RikVN/AMR
공식 코드https://github.com/RikVN/DRS_parsing
공식 코드https://github.com/RikVN/Neural_DRS
공식 코드Exploring Neural Methods for Parsing Discourse Representation Structures
https://github.com/SamuelGabriel/sqlbpe
공식 코드https://github.com/Satissss/LinkAlign
공식 코드LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL
https://github.com/Shivanshu-Gupta/structural-diversity
공식 코드Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation
https://github.com/SijieSong/person_generation_spt
공식 코드Unsupervised Person Image Generation with Semantic Parsing Transformation
https://github.com/StanleySun233/maritime-vts
공식 코드https://github.com/TellinaTool/nl2bash
공식 코드NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
https://github.com/ToLightUpTheSky/ParsingDST
공식 코드https://github.com/VinAIResearch/ViText2SQL
공식 코드A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
https://github.com/XGenerationLab/XiYan-SQL
공식 코드A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/Yale-LILY/FeTaQA
공식 코드https://github.com/Yale-LILY/dart
공식 코드https://github.com/Zhangjinso/PINet_PG
공식 코드https://github.com/ZhaoFuheng/LLM-SQL-Solver
공식 코드https://github.com/ablodge/leamr
공식 코드https://github.com/adamkhakhar/python-pac-code-prediction-set
공식 코드https://github.com/adrianbzg/sqlformer
공식 코드SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
https://github.com/aghie/tb-amr
공식 코드https://github.com/ahmedheakl/araspider
공식 코드https://github.com/akshittyagi/intclass
공식 코드Fast Intent Classification for Spoken Language Understanding
https://github.com/alibaba/route
공식 코드ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
https://github.com/alibabaresearch/damo-convai
공식 코드Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.