https://github.com/parkervg/destt5
공식 코드Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
Papers with Code 아카이브
Semantic Parsing
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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/pcyin/tranX
공식 코드TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
https://github.com/percyliang/sempre
공식 코드https://github.com/phddamuge/UniRPG
공식 코드UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
https://github.com/pkucoli/srl4il
공식 코드https://github.com/pkunlp-icler/amr_reverse_graph_linearization
공식 코드https://github.com/pkunlp-icler/atp
공식 코드ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
https://github.com/pkunlp-icler/kid
공식 코드https://github.com/pkunlp-icler/two-stage-camrp
공식 코드https://github.com/psoulos/dtm
공식 코드Differentiable Tree Operations Promote Compositional Generalization
https://github.com/psunlpgroup/xsemplr
공식 코드XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
https://github.com/pth1993/vnSRL
공식 코드https://github.com/qjay612/meta_learning_NL2SQL
공식 코드Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
https://github.com/quge2023/TA-SQL
공식 코드https://github.com/rhythmcao/semantic-parsing-dual
공식 코드https://github.com/rhythmcao/text2sql-lgesql
공식 코드LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
https://github.com/rhythmcao/unsup-two-stage-semantic-parsing
공식 코드Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
https://github.com/rshin/seq2struct
공식 코드Program Synthesis and Semantic Parsing with Learned Code Idioms
https://github.com/rubenkruiper/spar.txt
공식 코드SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts
https://github.com/ruc-datalab/rewardsql
공식 코드Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards
https://github.com/ruckbreasoning/codes
공식 코드CodeS: Towards Building Open-source Language Models for Text-to-SQL
https://github.com/ruckbreasoning/resdsql
공식 코드RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
https://github.com/ruiqi-zhong/TestSuiteEval
공식 코드Semantic Evaluation for Text-to-SQL with Distilled Test Suites
https://github.com/ruiqi-zhong/emnlp23-apel
공식 코드Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL
https://github.com/runnerup96/error-detection-in-text2sql
공식 코드Confidence Estimation for Error Detection in Text-to-SQL Systems
https://github.com/runzewang/IST-SQL
공식 코드Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing
https://github.com/ryanzhumich/editsql
공식 코드https://github.com/sagarverma/FashionRecommendationST-LSTM
공식 코드https://github.com/salesforce/TabularSemanticParsing
공식 코드Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
https://github.com/salesforce/WikiSQL
공식 코드Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/salt-nlp/damp
공식 코드DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
https://github.com/sanxing-chen/linking-tale
공식 코드https://github.com/saparina/ambrosia
공식 코드AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries
https://github.com/saparina/disambiguate-then-parse
공식 코드https://github.com/saparina/text2sql-nlvariation
공식 코드Improving Generalization in Semantic Parsing by Increasing Natural Language Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.