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Table-to-Text Generation
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**Here is the provided data converted into a table format for clarity: COUNTRIES 1971-2010 2011 2012 2013 2014 2015 2016 2017 2018
Saudi Arabia 2742962 222247 358560 270502 312489 522750 462598 143363 100910 U.A.E 1595574 156353 182630 273234 350522 326986 295647 275436 208635 Oman 394436 53525 69407 47794 39793 47788 45085 42362 27202 Qatar 82043 5121 7320 8119 10042 12741 9706 11592 20993 Bahrain 94599 10641 10530 9600 9226 9029 8226 7919 5745 Kuwait 180755 173 5 229 132 164 770 773 493 South Korea 15343 12 7 12 46 13 17 9 13 Malaysia 23410 2092 1309 2031 20577 20216 10625 7174 9881 China 1717 180 220 155 254 355 482 457 854 Algeria 878 7 2 7 36 211 259 461 213 Angola 601 8 6 8 1 22 22 12 11 Azerbaijan 51 0 3 98 22 8 8 8 20 Brunei 998 79 74 67 48 85 85 212 225 Cameroon 48 15 0 0 3 2 0 1 4 Croatia 44 1 0 0 0 0 0 0 0 Cyprus 922 71 129 111 278 500 990 1729 1644 Gabon 299 2 4 1 8 0 0 2 0 Gen-Island 195 0 0 0 0 2 0 0 0 Germany 187 11 23 26 23 43 38 64 103 Greece 542 0 0 0 0 2 3 2 3 Guinea 144 15 12 13 6 10 11 6 11 Hong Kong 252 26 17 20 38 29 38 54 57 Iran 12586 14 3 26 5 65 37 100 20 Iraq 68135 0 32 951 1041 709 543 599 756 Italy 17763 2875 3361 2068 1563 431 242 141 86 Japan 380 48 62 44 69 82 102 153 258 Jordan 5341 178 279 345 328 321 282 285 170 Kenya 67 11 8 6 3 11 15 8 17 Lebanon 432 30 23 15 57 33 42 24 27 Libya 72112 490 1872 4543 2121 8 0 4 8 Morocco 44 0 0 0 2 0 0 1 5 Nigeria 2665 166 142 117 113 106 104 75 115
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EagleW/Stage-wise-Fine-tuning
공식 코드https://github.com/GXimingLu/a_star_neurologic
공식 코드NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
https://github.com/KaijuML/dtt-multi-branch
공식 코드Controlling Hallucinations at Word Level in Data-to-Text Generation
https://github.com/ReneeYe/VariationalTemplateMachine
공식 코드https://github.com/UKPLab/plms-graph2text
공식 코드Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/XiangLi1999/PrefixTuning
공식 코드https://github.com/alonsoapp/pixt3
공식 코드https://github.com/alonsoapp/tlt
공식 코드Automatic Logical Forms improve fidelity in Table-to-Text generation
https://github.com/atulkum/pointer_summarizer
공식 코드Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
https://github.com/bernard-yang/distilltablecot
공식 코드Effective Distillation of Table-based Reasoning Ability from LLMs
https://github.com/ernestgong/data2text-three-dimensions
공식 코드https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research/google-research/tree/master/etcsum
공식 코드Stepwise Extractive Summarization and Planning with Structured Transformers
https://github.com/h-shahidi/2birds-gen
공식 코드https://github.com/hanxuhu/controllt2trobust
공식 코드Improving User Controlled Table-To-Text Generation Robustness
https://github.com/kasnerz/tabgenie
공식 코드https://github.com/lancopku/Pivot
공식 코드Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
https://github.com/liang8qi/plan-then-seam
공식 코드https://github.com/luka-group/lattice
공식 코드Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
https://github.com/microsoft/plog
공식 코드PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation
https://github.com/ramber1836/tasd
공식 코드https://github.com/shalei120/OxCTE
공식 코드Controlling Text Edition by Changing Answers of Specific Questions
https://github.com/sjtugzx/akg
공식 코드https://github.com/tyliupku/wiki2bio
공식 코드Table-to-text Generation by Structure-aware Seq2seq Learning
https://github.com/ukplab/emnlp2022-reasoning-aware-pretraining
공식 코드Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
https://github.com/ukplab/starsem2023-arithmetic-based-pretraining
공식 코드Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
https://github.com/wentinghome/amg
공식 코드Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots
https://github.com/worldhellow/hallucinations-c2t
공식 코드https://github.com/yale-lily/loft
공식 코드LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
https://github.com/yale-lily/reastap
공식 코드ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
https://github.com/yale-nlp/llm-t2t
공식 코드https://github.com/yale-nlp/qtsumm
공식 코드https://github.com/yilunzhao/llm-t2t
공식 코드https://github.com/yilunzhao/qtsumm
공식 코드https://github.com/yxuansu/few-shot-table-to-text-generation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.