Search Results for author:
Found 9 papers, 7 papers with code
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases
Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry.
A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation
These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.
The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation
Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system.
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.
TwistList: Resources and Baselines for Tongue Twister Generation
Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry.
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
In this paper, we propose a novel context-aware graph-attention model (Context-aware GAT), which can effectively incorporate global features of relevant knowledge graphs based on a context-enhanced knowledge aggregation process.
Terminology-aware Medical Dialogue Generation
In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
In spite of the success of prior works with the application of pre-trained models, current neural models for Chinese stories still struggle to generate high-quality long text narratives.
NGEP: A Graph-based Event Planning Framework for Story Generation
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i. e. storylines) to guide story generation.
