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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

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Showing 1–50 of 219 papers

TitleStatusHype
Large Language Models as Span Annotators—0
SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Data—0
An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation—0
Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language ModelCode1
Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation—0
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation—0
SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text GenerationCode0
Bridging the Gap between Different Vocabularies for LLM EnsembleCode1
Prompting for Numerical Sequences: A Case Study on Market Comment GenerationCode2
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text GenerationCode0
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language ModelsCode0
A Systematic Review of Data-to-Text NLG—0
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy—0
Self-training from Self-memory in Data-to-text GenerationCode0
Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation—0
Unifying Structured Data as Graph for Data-to-Text Pre-TrainingCode0
Prompt Optimization via Adversarial In-Context LearningCode1
Compositional Generalization for Data-to-Text Generation—0
PixT3: Pixel-based Table-To-Text GenerationCode0
TLM: Token-Level Masking for TransformersCode0
ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text GenerationCode0
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode0
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text GenerationCode0
Data-to-text Generation for Severely Under-Resourced Languages with GPT-3.5: A Bit of Help Needed from Google TranslateCode0
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning—0
Tackling Hallucinations in Neural Chart SummarizationCode0
Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark RemedyCode0
You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting—0
Faithful Low-Resource Data-to-Text Generation through Cycle TrainingCode0
Pruning Pre-trained Language Models with Principled Importance and Self-regularizationCode0
ReTAG: Reasoning Aware Table to Analytic Text Generation—0
Keras GPT Copilot: Integrating the Power of Large Language Models in Deep Learning Model DevelopmentCode1
Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
TabGenie: A Toolkit for Table-to-Text GenerationCode1
TextBox 2.0: A Text Generation Library with Pre-trained Language ModelsCode3
Commentary Generation from Data Records of Multiplayer Strategy Esports GameCode0
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation—0
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation—0
Time-aware Prompting for Text Generation—0
TaTa: A Multilingual Table-to-Text Dataset for African LanguagesCode2
Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences—0
Table-To-Text generation and pre-training with TabT5—0
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language ModelsCode1
Comparing Computational Architectures for Automated Journalism—0
Content Type Profiling of Data-to-Text Generation DatasetsCode0
Calibrating Sequence likelihood Improves Conditional Language Generation—0
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages—0
Show:102550
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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