Jihun Choi

Affiliations:
  • Seoul National University, Department oj Computer Science and Engineering, Korea


According to our database1, Jihun Choi authored at least 12 papers between 2016 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2020
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification.
CoRR, 2019

SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification.
Proceedings of the 13th International Workshop on Semantic Evaluation, 2019

Cell-aware Stacked LSTMs for Modeling Sentences.
Proceedings of The 11th Asian Conference on Machine Learning, 2019

A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Dynamic Compositionality in Recursive Neural Networks with Structure-Aware Tag Representations.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Element-wise Bilinear Interaction for Sentence Matching.
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, 2018

SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension.
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018

Learning to Compose Task-Specific Tree Structures.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Unsupervised Learning of Task-Specific Tree Structures with Tree-LSTMs.
CoRR, 2017

Partition-Based Clustering with Sliding Windows for Data Streams.
Proceedings of the Database Systems for Advanced Applications, 2017

2016
A grapheme-level approach for constructing a Korean morphological analyzer without linguistic knowledge.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016


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