SOTAVerified

Data-to-Text Generation

A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.

( Image credit: Data-to-Text Generation with Content Selection and Planning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 219 papers

TitleStatusHype
uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation—0
Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training—0
Utilising Knowledge Graph Embeddings for Data-to-Text Generation—0
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation—0
What Makes Data-to-Text Generation Hard for Pretrained Language Models?—0
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages—0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method—0
Pruning Pre-trained Language Models with Principled Importance and Self-regularizationCode0
Transition-Based Deep Input LinearizationCode0
R2D2: Robust Data-to-Text with Replacement DetectionCode0
The E2E Dataset: New Challenges For End-to-End GenerationCode0
Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text GenerationCode0
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text GenerationCode0
Improving Quality and Efficiency in Plan-based Neural Data-to-Text GenerationCode0
Improving Compositional Generalization with Self-Training for Data-to-Text GenerationCode0
How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation?Code0
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language ModelsCode0
Search and Learn: Improving Semantic Coverage for Data-to-Text GenerationCode0
Handling Rare Items in Data-to-Text GenerationCode0
Findings of the E2E NLG ChallengeCode0
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text GenerationCode0
Faithful Low-Resource Data-to-Text Generation through Cycle TrainingCode0
Learning to Select, Track, and Generate for Data-to-TextCode0
Learning with Contrastive Examples for Data-to-Text GenerationCode0
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode0
Long and Diverse Text Generation with Planning-based Hierarchical Variational ModelCode0
Selective Token Generation for Few-shot Natural Language GenerationCode0
Self-training from Self-memory in Data-to-text GenerationCode0
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-AttentionCode0
Semantic Noise Matters for Neural Natural Language GenerationCode0
SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text GenerationCode0
Creating a Corpus for Russian Data-to-Text Generation Using Neural Machine Translation and Post-EditingCode0
Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language InferenceCode0
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text GenerationCode0
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover DistanceCode0
A Hierarchical Model for Data-to-Text GenerationCode0
Neural data-to-text generation: A comparison between pipeline and end-to-end architecturesCode0
TLM: Token-Level Masking for TransformersCode0
Copy mechanism and tailored training for character-based data-to-text generationCode0
Content Type Profiling of Data-to-Text Generation DatasetsCode0
Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-TextCode0
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine AlignmentCode0
ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text GenerationCode0
Challenges in Data-to-Document GenerationCode0
Commentary Generation from Data Records of Multiplayer Strategy Esports GameCode0
Unifying Structured Data as Graph for Data-to-Text Pre-TrainingCode0
Tackling Hallucinations in Neural Chart SummarizationCode0
Enhancing AMR-to-Text Generation with Dual Graph RepresentationsCode0
Bootstrapping Generators from Noisy DataCode0
Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark RemedyCode0
Show:102550
← PrevPage 4 of 5Next →
All datasetsWebNLGE2E NLG ChallengeWebNLG FullCleaned E2E NLG ChallengeRotoWireRotoWire (Relation Generation)ToTToXAlignDARTMULTIWOZ 2.1RotoWire (Content Ordering)Rotowire (Content Selection)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Control Prefixes (A1, T5-large)BLEU67.32—Unverified
2Control Prefixes (A1, A2, T5-large)BLEU67.15—Unverified
3JointGT BaselineBLEU67.08—Unverified
4FactT5BBLEU67.04—Unverified
5T5B BaselineBLEU67.04—Unverified
6FactJointGTBLEU66.89—Unverified
7T5-large + Wiki + PositionBLEU66.07—Unverified
8HTML (fine-tuning)BLEU65.4—Unverified
9T5-smallBLEU65.05—Unverified
10TrICy (trK = trk* = 0.24)BLEU64.73—Unverified
#ModelMetricClaimedVerifiedStatus
1S_1^RBLEU68.6—Unverified
2EDA_CSBLEU67.05—Unverified
3TrICy (trK = 0)BLEU66.43—Unverified
4SlugBLEU66.19—Unverified
5TGenBLEU65.93—Unverified
6EDA_CS (TL)BLEU65.8—Unverified
7Sys1-PrimaryBLEU65.61—Unverified
8ZhangBLEU65.45—Unverified
9Self-memoryBLEU65.11—Unverified
10GongBLEU64.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Control Prefixes (A1, A2, T5-large)BLEU62.27—Unverified
2Control Prefixes (A1, T5-large)BLEU61.94—Unverified
3T5-large + Wiki + PositionBLEU60.56—Unverified
4T5-largeBLEU59.7—Unverified
5T5-LargeBLEU57.1—Unverified
6HTLM (prefix 0.1%)BLEU56.3—Unverified
7DATATUNER_NO_FCBLEU52.9—Unverified
8Transformer (Pipeline)BLEU51.68—Unverified
#ModelMetricClaimedVerifiedStatus
1Control Prefixes (T5-large)BLEU (Test set)44.15—Unverified
2DataTuner_FCBLEU (Test set)43.6—Unverified
3TGenBLEU (Test set)40.73—Unverified
4LSTMMETEOR (Validation set)0.39—Unverified
5TGenMETEOR (Validation set)0.39—Unverified
6BARTMETEOR (Validation set)0.37—Unverified
7T5METEOR (Validation set)0.37—Unverified
#ModelMetricClaimedVerifiedStatus
1HierarchicalEncoder + NR + IRBLEU17.96—Unverified
2Hierarchical transformer encoder + conditional copyBLEU17.5—Unverified
3Force-CopyBLEU17.26—Unverified
4Neural Content Planning + conditional copyBLEU16.5—Unverified
5MacroBLEU15.46—Unverified
6Encoder-decoder + conditional copyBLEU14.19—Unverified
#ModelMetricClaimedVerifiedStatus
1SeqPlanPrecision97.6—Unverified
2MacroPrecision97.6—Unverified
3Force-CopyPrecision95.4—Unverified
4Hierarchical Transformer Encoder + conditional copyPrecision89.46—Unverified
5Neural Content Planning + conditional copyPrecision87.47—Unverified
6Encoder-decoder + conditional copyPrecision74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1T5-3BBLEU49.5—Unverified
2LATTICE (T5-base)BLEU48.4—Unverified
3BERT-to-BERTBLEU44—Unverified
4Pointer GeneratorBLEU41.6—Unverified
5NCP+CC (Puduppully et al 2019)BLEU19.2—Unverified
6T5METEOR0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Fact-aware embedding with mT5BLEU429.27—Unverified
2Bi-lingual mT5BLEU425.88—Unverified
3mT5BLEU425—Unverified
4Vanilla TransformerBLEU419.9—Unverified
5Translate-Output mT5BLEU418.91—Unverified
6Graph Attention Network Encoder +Transformer DecoderBLEU418.3—Unverified
#ModelMetricClaimedVerifiedStatus
1T5B BaselineBLEU48.47—Unverified
2FactT5BBLEU48.37—Unverified
3self-mem + new dataBLEU47.76—Unverified
4JointGT BaselineBLEU47.51—Unverified
5FactJointGTBLEU47.39—Unverified
#ModelMetricClaimedVerifiedStatus
1T5-BaseBLEU35.1—Unverified
2T5-smallBLEU34.96—Unverified
3T2G2BLEU34.91—Unverified
4SC-GPT2BLEU30.76—Unverified
5HDSABLEU26.48—Unverified
#ModelMetricClaimedVerifiedStatus
1Hierarchical Transformer Encoder + conditional copyDLD18.9—Unverified
2Neural Content Planning + conditional copyDLD18.58—Unverified
3MacroDLD17.7—Unverified
4Force-CopyDLD17.26—Unverified
5Encoder-decoder + conditional copyDLD8.68—Unverified
#ModelMetricClaimedVerifiedStatus
1Hierarchical Transformer Encoder + conditional copyPrecision39.47—Unverified
2Force-CopyPrecision34.34—Unverified
3Neural Content Planning + conditional copyPrecision34.18—Unverified
4MacroPrecision34.1—Unverified
5Encoder-decoder + conditional copyPrecision29.49—Unverified
#ModelMetricClaimedVerifiedStatus
1SeqPlanBLEU14.29—Unverified
2MacroBLEU12.62—Unverified
3ENTBLEU11.5—Unverified
4Force-CopyBLEU10.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SeqPlanDLD22.7—Unverified
2MacroDLD21.8—Unverified
3Force-CopyDLD21.16—Unverified
4ENTDLD20.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SeqPlanPrecision95.9—Unverified
2MacroPrecision94.4—Unverified
3Force-CopyPrecision84.5—Unverified
4ENTPrecision81.1—Unverified
#ModelMetricClaimedVerifiedStatus
1binmtBLEU score26.35—Unverified
2tgenBLEU score21.96—Unverified
3massBLEU score17.72—Unverified
#ModelMetricClaimedVerifiedStatus
1Force-CopyPrecision49.39—Unverified
2SeqPlanPrecision43.3—Unverified
3MacroPrecision40.8—Unverified
#ModelMetricClaimedVerifiedStatus
1self-mem + new data (random)METEOR46.11—Unverified
2self-mem + new data (fixed)METEOR46.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Transition based Deep Input LinearizationBLEU80.49—Unverified
2GCN + featBLEU0.67—Unverified
#ModelMetricClaimedVerifiedStatus
1DataTuner_FCBLEU53.6—Unverified
2Bo3BLEU52.1—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTMETEOR0.46—Unverified
2mT5METEOR0.29—Unverified
#ModelMetricClaimedVerifiedStatus
1mBARTMETEOR0.61—Unverified
2mT5METEOR0.18—Unverified
#ModelMetricClaimedVerifiedStatus
1StructAdaptBleu48—Unverified
#ModelMetricClaimedVerifiedStatus
1T5-largeBLEU45.85—Unverified
#ModelMetricClaimedVerifiedStatus
1T5-largeBLEU69.27—Unverified
#ModelMetricClaimedVerifiedStatus
1OursBLEU24.56—Unverified