Search Results for author: Sung-Min Lee

Found 3 papers, 2 papers with code

JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts

no code implementations • SemEval (NAACL) 2022 • Daewook Kang, Sung-Min Lee, Eunhwan Park, Seung-Hoon Na

In this study, we examine the ability of contextualized representations of pretrained language model to distinguish whether sequences from instructional articles are plausible or implausible.

Language Modelling Multi-class Classification +1

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JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions

1 code implementation • SemEval (NAACL) 2022 • Sung-Min Lee, Seung-Hoon Na

This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and relation extraction (RE).

Ranked #1 on Joint Entity and Relation Extraction on SemEval-2022 Task-12

Joint Entity and Relation Extraction Machine Reading Comprehension +1

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MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data

1 code implementation • Conference 2023 • Sung-Min Lee, Eunhwan Park, Daeryong Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na

Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems.

Ranked #1 on Question Answering on HybridQA

Decoder Question Answering

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