Conditional Text Generation
27 papers with code • 1 benchmarks • 4 datasets
The task of generating text according to some pre-specified conditioning (e.g. topic or sentiment or constraint)
Benchmarks
These leaderboards are used to track progress in Conditional Text Generation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
VitaminC
WikiGraphs
Lipogram-e
BalitaNLP
Most implemented papers
Pragmatically Informative Text Generation
sIncerass/prag_generation
•
•
NAACL 2019
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics.
Unifying Vision-and-Language Tasks via Text Generation
j-min/VL-T5
•
•
On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models.
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
morningmoni/ede
•
•
EMNLP 2021
In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial.
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
gicheonkang/gst-visdial
•
•
CVPR 2023
As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1. 2M to 12. 9M QA data).
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
beyondguo/genius
•
•
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by mask tokens).
Generating Text through Adversarial Training using Skip-Thought Vectors
Attempts have been made to utilize GANs with word embeddings for text generation.
Encoder-Agnostic Adaptation for Conditional Language Generation
harvardnlp/encoder-agnostic-adaptation
•
•
Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a pretrained model for arbitrary downstream tasks.
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
WHUIR/PPVAE
•
•
ACL 2020
Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.
ToTTo: A Controlled Table-To-Text Generation Dataset
We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
