24 citations · 89 across the 19 of their papers we have counts for
13 papers · 1 filter
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs)…
Metaphor Detection with Effective Context Denoising
Shun Wang, Yucheng Li, Chenghua Lin +2
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantic…
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
Yucheng Li, Shun Wang, Chenghua Lin +2
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only ac…
The Secret of Metaphor on Expressing Stronger Emotion
Yucheng Li, Frank Guerin, Chenghua Lin
Metaphors are proven to have stronger emotional impact than literal expressions. Although this conclusion is shown to be promising in benefiting various NLP applications, the reaso…
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
Chen Tang, Chenghua Lin, Henglin Huang +2
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence. Despite recent progress, current st…
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
Henglin Huang, Chen Tang, Tyler Loakman +2
Story generation aims to generate a long narrative conditioned on a given input. In spite of the success of prior works with the application of pre-trained models, current neural m…
NGEP: A Graph-based Event Planning Framework for Story Generation
Chen Tang, Zhihao Zhang, Tyler Loakman +2
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior wor…
Terminology-aware Medical Dialogue Generation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires…
Nominal Metaphor Generation with Multitask Learning
Yucheng Li, Chenghua Lin, Frank Geurin
Metaphor generation is a challenging task which can impact many downstream tasks such as improving user satisfaction with dialogue systems and story generation. This paper tackles…
Recent Advances in Neural Text Generation: A Task-Agnostic Survey
Chen Tang, Frank Guerin, Chenghua Lin
In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generat…
Interpreting Verbal Metaphors by Paraphrasing
Rui Mao, Chenghua Lin, Frank Guerin
Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks. Previous works showed that paraphrasing a metaphor as its litera…
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification
Rui Mao, Chenghua Lin, Frank Guerin
We tackle the problem of identifying metaphors in text, treated as a sequence tagging task. The pre-trained word embeddings GloVe, ELMo and BERT have individually shown good perfor…
BERT-hLSTMs: BERT and Hierarchical LSTMs for Visual Storytelling
Jing Su, Qingyun Dai, Frank Guerin +1
Visual storytelling is a creative and challenging task, aiming to automatically generate a story-like description for a sequence of images. The descriptions generated by previous v…