SOTAVerified

Text Generation

Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.

Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.

Further readings:

( Image credit: Adversarial Ranking for Language Generation )

Papers

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Showing 3901–3950 of 5335 papers

TitleStatusHype
Tweet to News Conversion: An Investigation into Unsupervised Controllable Text Generation—0
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMRCode0
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement—0
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems—0
Perception Score, A Learned Metric for Open-ended Text Generation Evaluation—0
Neural Language Generation: Formulation, Methods, and Evaluation—0
Defining and Evaluating Fair Natural Language Generation—0
Bollyrics: Automatic Lyrics Generator for Romanised HindiCode0
SBAT: Video Captioning with Sparse Boundary-Aware Transformer—0
Toward Givenness Hierarchy Theoretic Natural Language Generation—0
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR—0
Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners—0
CoreGen: Contextualized Code Representation Learning for Commit Message GenerationCode0
HTSS: A Novel Hybrid Text Summarisation and Simplification ArchitectureCode0
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo MethodsCode0
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation—0
Unsupervised Text Generation by Learning from Search—0
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation—0
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation—0
Paraphrase Generation by Learning How to Edit from Samples—0
Simple and Effective Retrieve-Edit-Rerank Text Generation—0
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks—0
Heterogeneous Graph Transformer for Graph-to-Sequence Learning—0
Understanding the Impact of Experiment Design for Evaluating Dialogue System Output—0
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation—0
Stylized Text Generation: Approaches and Applications—0
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network—0
Neural Multi-task Text Normalization and Sanitization with Pointer-Generator—0
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study—0
Smart To-Do: Automatic Generation of To-Do Items from Emails—0
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation—0
Adversarial Mutual Information for Text GenerationCode0
Building Production-Quality NLG Models with Minimal Labelled Data—0
Evaluation of Text Generation: A Survey—0
Neural Machine Translation For Paraphrase Generation—0
Automating Text Naturalness Evaluation of NLG Systems—0
Efficient text generation of user-defined topic using generative adversarial networks—0
Shared Task on Evaluating Accuracy in Natural Language Generation—0
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Explainable and Discourse Topic-aware Neural Language UnderstandingCode0
De-Anonymizing Text by Fingerprinting Language Generation—0
Fine-grained Sentiment Controlled Text Generation—0
Automatically Ranked Russian Paraphrase Corpus for Text Generation—0
Record-to-Text Generation with Style Imitation—0
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs—0
A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms—0
Transparency in Language Generation: Levels of Automation—0
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction—0
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech—0
ColdGANs: Taming Language GANs with Cautious Sampling Strategies—0
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All datasetsDARTCOCO CaptionsEMNLP2017 WMTReDialCommonGenROCStoriesChinese PoemsCzech restaurant informationOpenWebTextSciQYahoo QuestionsADGEN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1T5B BaselineBLEU48.74—Unverified
2FactT5BBLEU48.37—Unverified
3JointGT BaselineBLEU47.51—Unverified
4FactJointGTBLEU47.39—Unverified
5Control Prefixes (T5-large)METEOR0.41—Unverified
6T5METEOR0.12—Unverified
7BARTMETEOR0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1LeakGANBLEU-20.95—Unverified
2partGANBLEU-20.91—Unverified
3RankGANBLEU-20.85—Unverified
4RelGAN (100)BLEU-20.85—Unverified
5SeqGANBLEU-20.83—Unverified
#ModelMetricClaimedVerifiedStatus
1LeakGANBLEU-20.96—Unverified
2PPOGANBLEU-20.91—Unverified
3RelGANBLEU-20.88—Unverified
4SeqGANBLEU-20.86—Unverified
5RankGANBLEU-20.78—Unverified
#ModelMetricClaimedVerifiedStatus
1UniCRSDistinct-30.65—Unverified
2CRFRDistinct-30.52—Unverified
3KGSFDistinct-30.43—Unverified
4C2CRSDistinct-30.33—Unverified
5KBRDDistinct-30.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UniLMCIDEr14.92—Unverified
2BART (TextBox 2.0)CIDEr12.98—Unverified
3BARTMETEOR0.3—Unverified
4T5METEOR0.29—Unverified
#ModelMetricClaimedVerifiedStatus
1Beam search + A*esque (beam)BLEU-134.4—Unverified
2Beam search + A*esque (sample)BLEU-134.4—Unverified
3Beam search + A*esque (greedy)BLEU-134.3—Unverified
4Beam searchBLEU-133.7—Unverified
#ModelMetricClaimedVerifiedStatus
1RankGANBLEU-20.81—Unverified
2SeqGANBLEU-20.74—Unverified
3LeakGANBLEU-20.46—Unverified
#ModelMetricClaimedVerifiedStatus
1TGen++METEOR0.17—Unverified
2TGenMETEOR0.15—Unverified
3TGen+METEOR0.15—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT2-124Meval_loss3.12—Unverified
2GPT2-81M-LOOPeval_loss3.11—Unverified
3GPT2-Hermiteeval_loss2.91—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA-65B+CFG (zero-shot)Accuracy96.6—Unverified
2LLaMA-30B+CFG (zero-shot)Accuracy96.4—Unverified
3LLaMA-13B+CFG (zero-shot)Accuracy95.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN-VAENLL332.1—Unverified
2SA-VAENLL327.5—Unverified
3Aggressive VAENLL326.7—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)BLEU-410.2—Unverified
#ModelMetricClaimedVerifiedStatus
1STWGAN-GPBLEU-30.62—Unverified
#ModelMetricClaimedVerifiedStatus
1PALMROUGE-L41.41—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)ROUGE-L64.34—Unverified
#ModelMetricClaimedVerifiedStatus
1AEM+AttentionBLEU-114.17—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4ASR65.1—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)ROUGE-L42.96—Unverified
#ModelMetricClaimedVerifiedStatus
1Graph2SeqBLEU22—Unverified
#ModelMetricClaimedVerifiedStatus
1WGANGP + DGflowJS-40.19—Unverified