{"total":16,"rows":[{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409,"area":null},{"id":"2105.04623","title":"Improving Factual Consistency of Abstractive Summarization via Question Answering","created":"2021-05-10","cited_by_count":55,"area":null},{"id":"2305.13002","title":"Rethinking Semi-supervised Learning with Language Models","created":"2023-05-22","cited_by_count":17,"area":null},{"id":"2203.03047","title":"Recent Advances in Neural Text Generation: A Task-Agnostic Survey","created":"2022-03-06","cited_by_count":14,"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":"2109.03892","title":"Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models","created":"2021-09-08","cited_by_count":9,"area":null},{"id":"2106.05580","title":"AGGGEN: Ordering and Aggregating while Generating","created":"2021-06-10","cited_by_count":7,"area":null},{"id":"2110.08094","title":"Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue","created":"2021-10-15","cited_by_count":6,"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":"2402.17298","title":"ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks","created":"2024-02-27","cited_by_count":4,"area":null},{"id":"2205.09661","title":"Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2207.06839","title":"Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model","created":"2022-07-14","cited_by_count":1,"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":"2410.06348","title":"Harnessing the Power of Noise: A Survey of Techniques and Applications","created":"2024-10-08","cited_by_count":0,"area":null}],"next_cursor":null}