SOTAVerified

Table-to-Text Generation

**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

Papers

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Showing 1–50 of 68 papers

TitleStatusHype
The Challenge of Achieving Attributability in Multilingual Table-to-Text Generation with Question-Answer Blueprints—0
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization—0
Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models—0
Table-to-Text Generation with Pretrained Diffusion Models—0
Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation—0
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Towards Controlled Table-to-Text Generation with Scientific Reasoning—0
PixT3: Pixel-based Table-To-Text GenerationCode0
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation—0
Automatic Logical Forms improve fidelity in Table-to-Text generationCode0
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode0
Effective Distillation of Table-based Reasoning Ability from LLMsCode0
Tackling Hallucinations in Neural Chart SummarizationCode0
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking ScenariosCode1
QTSumm: Query-Focused Summarization over Tabular DataCode1
ReTAG: Reasoning Aware Table to Analytic Text Generation—0
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation—0
TabGenie: A Toolkit for Table-to-Text GenerationCode1
Adapting Knowledge for Few-shot Table-to-Text GenerationCode0
Improving User Controlled Table-To-Text Generation RobustnessCode0
Plan-then-Seam: Towards Efficient Table-to-Text GenerationCode0
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization—0
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating ApproachCode0
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning ExamplesCode1
Table-To-Text generation and pre-training with TabT5—0
Few-Shot Table-to-Text Generation with Prefix-Controlled Generator—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text GenerationCode1
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint—0
Diversity Enhanced Table-to-Text Generation via Type Control—0
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language ModelsCode0
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance LearningCode1
What Makes Good In-Context Examples for GPT-3?Code0
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few ShotsCode0
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning—0
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead HeuristicsCode1
FLAP: Table-to-Text Generation with Feature Indication and Numerical Reasoning Pretraining—0
Few-Shot Table-to-Text Generation with Prototype MemoryCode1
Structure-Aware Pre-Training for Table-to-Text Generation—0
Text-in-Context: Token-Level Error Detection for Table-to-Text GenerationCode0
Towards Table-to-Text Generation with Numerical ReasoningCode1
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?Code0
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation—0
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text GenerationCode1
HTLM: Hyper-Text Pre-Training and Prompting of Language Models—0
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation—0
Controlling Text Edition by Changing Answers of Specific QuestionsCode0
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification—0
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric ViewCode0
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All datasetsDARTWikiBioE2EWebNLG (All)WebNLG (Seen)WebNLG (Unseen)Wikipedia Person and Animal DatasetRotoWire

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1T5B BaselineMETEOR0.41—Unverified
2FactT5BMETEOR0.41—Unverified
3JointGT BaselineMETEOR0.4—Unverified
4FactJointGTMETEOR0.4—Unverified
5GPT-2-Large (fine-tuning)METEOR0.39—Unverified
6HTLM (fine-tuning)METEOR0.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Field-gating Seq2seq + dual attentionBLEU44.89—Unverified
2Field-gating Seq2seq + dual attention + beam searchBLEU44.71—Unverified
3MBDBLEU41.56—Unverified
4Table NLMBLEU34.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HTLM (fine-tuning)BLEU70.3—Unverified
2GPT-2-Large (fine-tuning)BLEU68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HTLM (fine-tuning)BLEU55.6—Unverified
2GPT-2-Large (fine-tuning)BLEU55.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HTLM (fine-tuning)BLEU65.4—Unverified
2GPT-2-Large (fine-tuning)BLEU65.3—Unverified
#ModelMetricClaimedVerifiedStatus
1HTLM (fine-tuning)BLEU48.4—Unverified
2GPT-2-Large (fine-tuning)BLEU43.1—Unverified
#ModelMetricClaimedVerifiedStatus
1VTMBLEU25.22—Unverified
2KB-to-Language Generation ModelBLEU23.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HierarchicalEncoder + NR + IR Content Ordering25.3—Unverified