Timothy Liu


2022

pdf bib
Towards Better Characterization of Paraphrases
Timothy Liu | De Wen Soh
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset. We also perform a detailed study on MRPC and propose improvements to the dataset, showing that it improves generalizability of models trained on the dataset. Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models.

2021

pdf bib
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 http://esra.cp.eng.chula.ac.th/.