{"total":35,"rows":[{"id":"2201.05273","title":"Pretrained Language Models for Text Generation: A Survey","created":"2022-01-14","cited_by_count":122,"area":null},{"id":"2210.07321","title":"Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods","created":"2022-10-13","cited_by_count":79,"area":null},{"id":"2103.09120","title":"Structural Adapters in Pretrained Language Models for AMR-to-text Generation","created":"2021-03-16","cited_by_count":48,"area":null},{"id":"2210.12814","title":"RuCoLA: Russian Corpus of Linguistic Acceptability","created":"2022-10-23","cited_by_count":26,"area":null},{"id":"2110.08329","title":"Control Prefixes for Parameter-Efficient Text Generation","created":"2021-10-15","cited_by_count":24,"area":null},{"id":"2108.00577","title":"Logic-Consistency Text Generation from Semantic Parses","created":"2021-08-02","cited_by_count":23,"area":null},{"id":"2203.16279","title":"Neural Pipeline for Zero-Shot Data-to-Text Generation","created":"2022-03-30","cited_by_count":19,"area":null},{"id":"2307.14712","title":"Evaluating Generative Models for Graph-to-Text Generation","created":"2023-07-27","cited_by_count":16,"area":null},{"id":"2210.04325","title":"ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models","created":"2022-10-09","cited_by_count":14,"area":null},{"id":"2102.03551","title":"Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling","created":"2021-02-06","cited_by_count":11,"area":null},{"id":"2105.08021","title":"Stage-wise Fine-tuning for Graph-to-Text Generation","created":"2021-05-17","cited_by_count":11,"area":null},{"id":"2106.00791","title":"DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation","created":"2021-06-01","cited_by_count":11,"area":null},{"id":"2109.07043","title":"Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG","created":"2021-09-15","cited_by_count":11,"area":null},{"id":"2207.12571","title":"Innovations in Neural Data-to-text Generation: A Survey","created":"2022-07-25","cited_by_count":10,"area":null},{"id":"2205.11505","title":"What Makes Data-to-Text Generation Hard for Pretrained Language Models?","created":"2022-05-23","cited_by_count":8,"area":null},{"id":"2310.16964","title":"Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation","created":"2023-10-25","cited_by_count":7,"area":null},{"id":"2210.07373","title":"Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models","created":"2022-10-13","cited_by_count":6,"area":null},{"id":"2401.07897","title":"The Pitfalls of Defining Hallucination","created":"2024-01-15","cited_by_count":5,"area":null},{"id":"2205.02627","title":"WDV: A Broad Data Verbalisation Dataset Built from Wikidata","created":"2022-05-05","cited_by_count":4,"area":null},{"id":"2307.14440","title":"Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking","created":"2023-07-26","cited_by_count":4,"area":null},{"id":"2402.08496","title":"A Systematic Review of Data-to-Text NLG","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2109.03808","title":"Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation","created":"2021-09-08","cited_by_count":3,"area":null},{"id":"2108.01487","title":"WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue","created":"2021-08-01","cited_by_count":2,"area":null},{"id":"2306.15933","title":"You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting","created":"2023-06-28","cited_by_count":2,"area":null},{"id":"2310.17279","title":"Automatic Logical Forms improve fidelity in Table-to-Text generation","created":"2023-10-26","cited_by_count":2,"area":null},{"id":"2404.06911","title":"GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism","created":"2024-04-10","cited_by_count":2,"area":null},{"id":"2105.04645","title":"R2D2: Relational Text Decoding with Transformers","created":"2021-05-10","cited_by_count":1,"area":null},{"id":"2301.12568","title":"Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation","created":"2023-01-29","cited_by_count":1,"area":null},{"id":"2307.01548","title":"Knowledge Graph for NLG in the context of conversational agents","created":"2023-07-04","cited_by_count":1,"area":null},{"id":"2308.15898","title":"An xAI Approach for Data-to-Text Processing with ASP","created":"2023-08-30","cited_by_count":1,"area":null},{"id":"2309.12294","title":"Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models","created":"2023-09-21","cited_by_count":1,"area":null},{"id":"2211.03648","title":"Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems","created":"2022-11-07","cited_by_count":0,"area":null},{"id":"2401.10567","title":"Self-training from Self-memory in Data-to-text Generation","created":"2024-01-19","cited_by_count":0,"area":null},{"id":"2402.01714","title":"TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy","created":"2024-01-25","cited_by_count":0,"area":null},{"id":"2605.23440","title":"SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction","created":"2026-05-22","cited_by_count":0,"area":null}],"next_cursor":null}