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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 101–150 of 219 papers

TitleStatusHype
Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text GenerationCode0
Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-TextCode0
A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge datasetCode0
A Hierarchical Model for Data-to-Text GenerationCode0
Tackling Hallucinations in Neural Chart SummarizationCode0
Template-free Data-to-Text Generation of Finnish Sports NewsCode0
Handling Rare Items in Data-to-Text GenerationCode0
The Code2Text Challenge: Text Generation in Source Code LibrariesCode0
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language ModelsCode0
The E2E Dataset: New Challenges For End-to-End GenerationCode0
Data-to-text Generation for Severely Under-Resourced Languages with GPT-3.5: A Bit of Help Needed from Google TranslateCode0
Data-to-Text Generation with Content Selection and PlanningCode0
You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting—0
1 Million Captioned Dutch Newspaper Images—0
A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG—0
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation—0
A Domain Agnostic Approach to Verbalizing n-ary Events without Parallel Corpora—0
Analysing Data-To-Text Generation Benchmarks—0
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation—0
An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation—0
A Repository of Data and Evaluation Resources for Natural Language Generation—0
A Statistical Framework for Product Description Generation—0
A Survey of Natural Language Generation—0
A Systematic Review of Data-to-Text NLG—0
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge—0
Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation—0
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation—0
Building RDF Content for Data-to-Text Generation—0
Calibrating Sequence likelihood Improves Conditional Language Generation—0
Category-Driven Content Selection—0
Comparing Computational Architectures for Automated Journalism—0
Compositional Generalization for Data-to-Text Generation—0
Content Selection in Data-to-Text Systems: A Survey—0
Creating Training Corpora for NLG Micro-Planners—0
Cross-lingual Alignment of Knowledge Graph Triples with Sentences—0
Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Data—0
Data2Text Studio: Automated Text Generation from Structured Data—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
Deep Learning Approaches to Text Production—0
Designing and testing the messages produced by a virtual dietitian—0
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning—0
DORB: Dynamically Optimizing Multiple Rewards with Bandits—0
Enhanced Transformer Model for Data-to-Text Generation—0
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge—0
Entity-Based Semantic Adequacy for Data-to-Text Generation—0
Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Exploring Structural Encoding for Data-to-Text Generation—0
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods—0
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning—0
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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