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Methodology
← Generation
Natural Language Processing
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Generation
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Text Generation
4534 directly classified papers
Papers per year
2011: 1
2015: 2
2016: 20
2017: 75
2018: 165
2019: 353
2020: 383
2021: 510
2022: 612
2023: 620
2024: 804
2025: 868
2026: 121
Papers
DLGNet: A Transformer-based Model for Dialogue Response Generation
ACL 2020
Meeting the 2020 Duolingo Challenge on a Shoestring
ACL 2020
Generating Fact Checking Explanations
ACL 2020
Diversifying Dialogue Generation with Non-Conversational Text
ACL 2020
Simultaneous paraphrasing and translation by fine-tuning Transformer models
ACL 2020
The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education
ACL 2020
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation
ACL 2020
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting
ACL 2020
Show, Edit and Tell: A Framework for Editing Image Captions
CVPR 2020
Say As You Wish: Fine-Grained Control of Image Caption Generation With Abstract Scene Graphs
CVPR 2020
Transform and Tell: Entity-Aware News Image Captioning
CVPR 2020
Context-Driven Satirical News Generation
ACL 2020
Visual Question Generation from Radiology Images
ACL 2020
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study
ACL 2020
INSET: Sentence Infilling with INter-SEntential Transformer
ACL 2020
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
ACL 2020
Cascaded Text Generation with Markov Transformers
NIPS 2020
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
NIPS 2020
CopyBERT: A Unified Approach to Question Generation with Self-Attention
ACL 2020
Automatically Ranked Russian Paraphrase Corpus for Text Generation
ACL 2020
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
ACL 2020
Toward Better Storylines with Sentence-Level Language Models
ACL 2020
Heterogeneous Graph Transformer for Graph-to-Sequence Learning
ACL 2020
On the Importance of Diversity in Question Generation for QA
ACL 2020
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
ACL 2020
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