Papers by Qingliang Chen
Pruning Adatperfusion with Lottery Ticket Hypothesis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are computationally expensive to fine-tune and require large storage. |
| Approach: | They propose a method to identify the influence of each adapter module and a way to prune adapters based on the Lottery Ticket Hypothesis. |
| Outcome: | The proposed model reduces size significantly while keeping performance intact. |
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences. |
| Approach: | They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs. |
| Outcome: | The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models. |