1 code implementation • COLING (TextGraphs) 2020 • 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.
2 code implementations • 20 Mar 2024 • Yew Ken Chia, Vernon Toh Yan Han, Deepanway Ghosal, Lidong Bing, Soujanya Poria
As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a collection of puzzles based on abstract patterns.
1 code implementation • 15 Nov 2023 • Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, Lidong Bing
Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
1 code implementation • 5 Jul 2023 • Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria
Interestingly, despite being introduced four years ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general problem-solving skills.
1 code implementation • NeurIPS 2023 • Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, Yew Ken Chia, Lidong Bing
M3Exam exhibits three unique characteristics: (1) multilingualism, encompassing questions from multiple countries that require strong multilingual proficiency and cultural knowledge; (2) multimodality, accounting for the multimodal nature of many exam questions to test the model's multimodal understanding capability; and (3) multilevel structure, featuring exams from three critical educational periods to comprehensively assess a model's proficiency at different levels.
2 code implementations • 7 Jun 2023 • Yew Ken Chia, Pengfei Hong, Lidong Bing, Soujanya Poria
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents.
no code implementations • 23 May 2023 • Yew Ken Chia, Hui Chen, Wei Han, Guizhen Chen, Sharifah Mahani Aljunied, Soujanya Poria, Lidong Bing
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • 22 May 2023 • Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, Lidong Bing
Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation.
no code implementations • 20 Dec 2022 • Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Shafiq Joty, Boyang Li, Lidong Bing
In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks.
1 code implementation • 18 Nov 2022 • Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria
Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.
Ranked #2 on Hyper-Relational Extraction on HyperRED
2 code implementations • Findings (ACL) 2022 • Yew Ken Chia, Lidong Bing, Soujanya Poria, Luo Si
We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.
Ranked #1 on Zero-shot Relation Triplet Extraction on Wiki-ZSL
2 code implementations • ACL 2021 • 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 term.
Ranked #5 on Aspect Sentiment Triplet Extraction on ASTE-Data-V2
Aspect Sentiment Triplet Extraction Computational Efficiency +1
1 code implementation • 28 Dec 2020 • 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.
1 code implementation • WS 2019 • 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.
no code implementations • 13 Sep 2019 • Martin Andrews, Yew Ken Chia, Sam Witteveen
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications.
no code implementations • NIPS Workshop CDNNRIA 2018 • Yew Ken Chia, Sam Witteveen, Martin Andrews
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018.