Aik Beng Ng


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NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines
Rohan Joseph | Timothy Liu | Aik Beng Ng | Simon See | Sunny Rai
Findings of the Association for Computational Linguistics: ACL 2023

Metaphors are highly creative constructs of human language that grow old and eventually die. Popular datasets used for metaphor processing tasks were constructed from dated source texts. In this paper, we propose NewsMet, a large high-quality contemporary dataset of news headlines hand-annotated with metaphorical verbs. The dataset comprises headlines from various sources including political, satirical, reliable and fake. Our dataset serves the purpose of evaluation for the tasks of metaphor interpretation and generation. The experiments reveal several insights and limitations of using LLMs to automate metaphor processing tasks as frequently seen in the recent literature. The dataset is publicly available for research purposes


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ESRA: Explainable Scientific Research Assistant
Pollawat Hongwimol | Peeranuth Kehasukcharoen | Pasit Laohawarutchai | Piyawat Lertvittayakumjorn | Aik Beng Ng | Zhangsheng Lai | Timothy Liu | Peerapon Vateekul
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv’s cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application at