Papers by Kwan Hui Lim
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification (2024.emnlp-industry)
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| Challenge: | Multi-turn intent classification is challenging due to the complexity and evolving nature of conversational contexts . lack of data on multi-turn datasets makes it difficult to collect multi-turned datasets a challenge . |
| Approach: | They propose a framework for multi-turn intent classification that integrates a retrieval-augmented mechanism with a fine-tuned smaller model. |
| Outcome: | The proposed framework improves accuracy on multi-turn intent classification tasks across six languages. |
An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)
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| Challenge: | Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. |
| Approach: | They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner. |
| Outcome: | The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks. |