Joohong Lee

TL;DR: This article proposed an end-to-end recurrent neural model that incorporates an entity-aware attention mechanism with a latent entity typing (LET) method, which not only effectively utilizes entities and their latent types as features, but also builds word representations by applying self-attention based on symmetrical similarity of a sentence itself.

TL;DR: This work proposes a novel end-to-end recurrent neural model which incorporates an entity-aware attention mechanism with a latent entity typing (LET) method and demonstrates that the model outperforms existing state-of-the-art models without any high-level features.

TL;DR: Experimental results demonstrate that a hybrid approach of morphological segmentation followed by BPE works best in Korean to/from English machine translation and natural language understanding tasks such as KorNLI, KorSTS, NSMC, and PAWS-X.

TL;DR: The Korean Language Understanding Evaluation (KLUE) benchmark as mentioned in this paper is a collection of 8 Korean NLP tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking.

TL;DR: In this article, a hybrid approach of morphological segmentation followed by Byte Pair Encoding (BPE) is proposed for Korean NLP tasks, and the results show that BPE segmentation is the most effective.