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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 151–200 of 219 papers

TitleStatusHype
Machine Translation Pre-training for Data-to-Text Generation -- A Case Study in Czech—0
Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training—0
A Hierarchical Model for Data-to-Text GenerationCode0
Semantic Noise Matters for Neural Natural Language GenerationCode0
Transition-Based Deep Input LinearizationCode0
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge—0
Enhanced Transformer Model for Data-to-Text Generation—0
Fine-Grained Control of Sentence Segmentation and Entity Positioning in Neural NLG—0
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation—0
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation—0
Template-free Data-to-Text Generation of Finnish Sports NewsCode0
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation—0
A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG—0
Neural Generation for Czech: Data and Baselines—0
Improving Quality and Efficiency in Plan-based Neural Data-to-Text GenerationCode0
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover DistanceCode0
Enhancing AMR-to-Text Generation with Dual Graph RepresentationsCode0
Neural data-to-text generation: A comparison between pipeline and end-to-end architecturesCode0
Long and Diverse Text Generation with Planning-based Hierarchical Variational ModelCode0
Creating a Corpus for Russian Data-to-Text Generation Using Neural Machine Translation and Post-EditingCode0
Learning to Select, Track, and Generate for Data-to-TextCode0
Data-to-text Generation with Entity ModelingCode0
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-AttentionCode0
Copy mechanism and tailored training for character-based data-to-text generationCode0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method—0
Text Generation with Exemplar-based Adaptive Decoding—0
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text GenerationCode0
Pragmatically Informative Text GenerationCode0
Data-to-Text Generation with Style ImitationCode0
Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methodsCode0
Designing and testing the messages produced by a virtual dietitian—0
E2E NLG Challenge: Neural Models vs. TemplatesCode0
Data2Text Studio: Automated Text Generation from Structured Data—0
Handling Rare Items in Data-to-Text GenerationCode0
End-to-End Content and Plan Selection for Data-to-Text GenerationCode0
Findings of the E2E NLG ChallengeCode0
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation—0
Generating Syntactic Paraphrases—0
Operations Guided Neural Networks for High Fidelity Data-To-Text GenerationCode0
Describing a Knowledge BaseCode0
Data-to-Text Generation with Content Selection and PlanningCode0
Point Precisely: Towards Ensuring the Precision of Data in Generated Texts Using Delayed Copy Mechanism—0
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data—0
Deep Learning Approaches to Text Production—0
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation—0
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation—0
Bootstrapping Generators from Noisy DataCode0
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge—0
Probabilistic Verb Selection for Data-to-Text Generation—0
Technical Report for E2E NLG Challenge—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