Papers by Jun-Seong Kim
Improving Multi-lingual Alignment Through Soft Contrastive Learning (2024.naacl-srw)
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| Challenge: | Existing methods to train multi-lingual sentence embeddings ruins the mono-lingual space. |
| Approach: | They propose a method to align multi-lingual embeddings based on similarity of sentences measured by a pre-trained mono-lingual teacher model. |
| Outcome: | The proposed method outperforms existing multi-lingual embeddings including LaBSE on five languages and on a translation pair for Tatoeba dataset. |
Modeling with Recurrent Neural Networks for Open Vocabulary Slots (C18-1)
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| Challenge: | Existing approaches to filling slots that take on values from a virtually unlimited set have been lacking in the natural language area. |
| Approach: | They propose a new attention-based recurrent neural network (RNN) model that captures the concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of . sentence. |
| Outcome: | The proposed model outperforms existing models with respect to discovering ‘open-vocabulary’ slots without any external information, such as a named entity database or knowledge base. |