26 citations · 51 across the 6 of their papers we have counts for
8 papers
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Yew Ken Chia, Pengfei Hong, Lidong Bing +1
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models,…
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied +2
Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as…
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria +1
Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task sett…
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction
Lu Xu, Yew Ken Chia, Lidong Bing
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion te…
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Yew Ken Chia, Sam Witteveen, Martin Andrews
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of metho…
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
Yew Ken Chia, Sam Witteveen, Martin Andrews
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's e…