Challenge: Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning.
Approach: They propose a Korean text-generation dataset for Korean generative commonsense reasoning and language model evaluation using a semi-automatic dataset construction approach.
Outcome: The proposed dataset is available at http://aihub.or.kr/opendata/korea-university.

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Challenge: Natural language understanding (NLU) and natural language generation (NLG) have opposite goals.
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KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding (2020.findings-emnlp)

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Challenge: Existing benchmark datasets for natural language inference and semantic textual similarity (STS) are not available in the Korean language.
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Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)

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Challenge: Recent advances in commonsense reasoning depend on large-scale human-authored training data.
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KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models (2024.findings-acl)

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Challenge: Language models are striving to grasp commonsense reasoning, but they are lacking in Korean commons- ense benchmarks.
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CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020.findings-emnlp)

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Challenge: Recent studies show that pre-trained language models perform well on commonsense-reasoning benchmark datasets, but building machines with commonsence to compose plausible sentences remains challenging.
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Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)

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Challenge: Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning.
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Korean Language Modeling via Syntactic Guide (2022.lrec-1)

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Challenge: Existing research on pre-trained language models focuses on widely-used languages . however, not every language can benefit from such models due to computational resources .
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)

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Challenge: In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge.
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)

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Challenge: a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets.
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A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)

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Challenge: This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field.
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