Papers with NLG model
DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)
Copied to clipboard
| Challenge: | Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses. |
| Approach: | They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
| Outcome: | The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
An Empirical Study of Generating Texts for Search Engine Advertising (2021.naacl-industry)
Copied to clipboard
| Challenge: | Existing studies on neural language generation have not evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. |
| Approach: | They propose to integrate a reinforcement learning framework into an end-to-end sequence-tosequence (Seq2S) model and demonstrate how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads. |
| Outcome: | The proposed method improves the ads’ impact, deploys the models to a product, and evaluates the generated ads. |
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | a new architecture for controlling, generating and augmenting text is being developed for supervised NLP tasks. |
| Approach: | They propose a conditional VAE architecture to control, generate, and augment text. |
| Outcome: | The proposed model shows high quality, diversity and attribute control in an ablation task. |
Injecting Entity Types into Entity-Guided Text Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in deep generative modeling have led to significant advances in natural language generation (NLG). |
| Approach: | They propose to model the entity type carefully in the decoding phase to generate contextual words accurately. |
| Outcome: | The proposed model produces a target sequence based on a given list of entities. |
Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for evaluating progress in natural language generation tasks are expensive, difficult to reproduce, and non-reusable. |
| Approach: | They propose a new automatic evaluation method for NLG called Near-Negative Distinction that repurposes prior human annotations into NND tests. |
| Outcome: | The proposed method achieves higher correlation with human judgments than standard NLG evaluation metrics. |
PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing systems produce generic, monotonic responses that lack individuality and fail to adapt to users’ personal attributes. |
| Approach: | They propose a dataset that incorporates user images as part of the persona, enabling personalized responses tailored to user-specific factors such as age or emotional context. |
| Outcome: | The proposed dataset enhances user experience, with personalized responses contributing to a more engaging interaction. |
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements. |
| Approach: | They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets. |
| Outcome: | The proposed approach improves on the SST-2, MRPC, YELP-2, and TREC-6 datasets. |