https://github.com/sapienzanlp/leakdistill
공식 코드Incorporating Graph Information in Transformer-based AMR Parsing
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/sasmohaimeed/sigma
공식 코드Sigma: A dataset for text-to-code semantic parsing with statistical analysis
https://github.com/sebischair/llm-sp-cqa
공식 코드https://github.com/seonhee99/ehr-seqsql
공식 코드EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
https://github.com/sgarg87/big_mech_isi_gg
공식 코드Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
https://github.com/sha-lab/duel
공식 코드Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks
https://github.com/shark-nlp/openicl
공식 코드https://github.com/shayantalaei/chess
공식 코드https://github.com/sheng-z/stog
공식 코드https://github.com/shijx12/kqapro_baselines
공식 코드https://github.com/shininglab/systematic-generalization-via-meaningful-learning
공식 코드https://github.com/shuaichenchang/odis-text-to-sql
공식 코드https://github.com/shuaichenchang/prompt-text-to-sql
공식 코드How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
https://github.com/shuailyu0110/sql-o1
공식 코드SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search
https://github.com/shuheikurita/semrl
공식 코드Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
https://github.com/sidaw/shrdlurn
공식 코드https://github.com/sivareddyg/graph-parser
공식 코드https://github.com/sivareddyg/udeplambda
공식 코드https://github.com/siyue-zhang/SynTableQA
공식 코드SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
https://github.com/smartdataanalytics/codecai
공식 코드Transformer with Tree-order Encoding for Neural Program Generation
https://github.com/snowflakedb/ArcticTraining
공식 코드ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback
https://github.com/snowflakedb/arcticinference
공식 코드SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
https://github.com/sohanpatnaik106/cabinet_qa
공식 코드CABINET: Content Relevance based Noise Reduction for Table Question Answering
https://github.com/songjw133/joint-loss-tuning-of-text-to-sql
공식 코드JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling
https://github.com/ss433s/sosweety
공식 코드https://github.com/stanford-oval/genie-toolkit
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/genienlp
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/schema2qa
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/wikidata-emnlp23
공식 코드https://github.com/sunlab-osu/MISP
공식 코드Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
https://github.com/sunlab-osu/reasonbert
공식 코드https://github.com/syxu828/QuestionAnsweringOverFB
공식 코드Question Answering on Freebase via Relation Extraction and Textual Evidence
https://github.com/tag-research/tag-bench
공식 코드https://github.com/tan92hl/complex-question-answering-evaluation-of-chatgpt
공식 코드https://github.com/tan92hl/complex-question-answering-evaluation-of-gpt-family
공식 코드https://github.com/taolusi/chisp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.