Jong-Hun Shin


JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing
Seung-Hoon Na | Jinwoon Min | Kwanghyeon Park | Jong-Hun Shin | Young-Kil Kim
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

This paper describes Jeonbuk National University (JBNU)’s system for the 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP 2019) at the Conference on Computational Natural Language Learning. Of the five frameworks, we address only the DELPH-IN MRS Bi-Lexical Dependencies (DP), Prague Semantic Dependencies (PSD), and Universal Conceptual Cognitive Annotation (UCCA) frameworks. We propose a unified parsing model using biaffine attention (Dozat and Manning, 2017), consisting of 1) a BERT-BiLSTM encoder and 2) a biaffine attention decoder. First, the BERT-BiLSTM for sentence encoder uses BERT to compose a sentence’s wordpieces into word-level embeddings and subsequently applies BiLSTM to word-level representations. Second, the biaffine attention decoder determines the scores for an edge’s existence and its labels based on biaffine attention functions between roledependent representations. We also present multi-level biaffine attention models by combining all the role-dependent representations that appear at multiple intermediate layers.


Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages
Gyu-Hyeon Choi | Jong-Hun Shin | Young-Kil Kim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Concept Equalization to Guide Correct Training of Neural Machine Translation
Kangil Kim | Jong-Hun Shin | Seung-Hoon Na | SangKeun Jung
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences. This approach is limited to find the best word composition and requires help of explicit methods as beam search. To help learning correct compositional mechanisms in NMTs, we propose concept equalization using direct mapping distributed representations of source and target sentences. In a translation experiment from English to French, the concept equalization significantly improved translation quality by 3.00 BLEU points compared to a state-of-the-art NMT model.


Semi-automatic Filtering of Translation Errors in Triangle Corpus
Sung-Kwon Choi | Jong-Hun Shin | Young-Gil Kim
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters