Papers by Grandee Lee
Modeling Code-Switch Languages Using Bilingual Parallel Corpus (2020.acl-main)
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| Challenge: | Existing models for bilingual language modeling are limited due to lack of training data and syntactic structure. |
| Approach: | They propose a bilingual attention language model that performs language modeling objective with a quasi-translation objective to model the monolingual and cross-lingual sequential dependency. |
| Outcome: | The proposed model reduces the perplexity of 20.5% over the best-reported model. |
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)
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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen
| Challenge: | mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events. |
| Approach: | They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously. |
| Outcome: | The proposed model performs better on four public datasets while saving time. |
DynaEval: Unifying Turn and Dialogue Level Evaluation (2021.acl-long)
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Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li
| Challenge: | Existing evaluation metrics focus on the turn-level quality of a dialogue . a unified framework that holistically considers the quality of the entire dialogue is needed . |
| Approach: | They propose a unified automatic evaluation framework which holistically considers the quality of the entire dialogue. |
| Outcome: | The proposed framework outperforms the state-of-the-art dialogue coherence model and correlates strongly with human judgements across multiple evaluation aspects at both turn and dialogue level. |
Revisiting Self-training for Few-shot Learning of Language Model (2021.emnlp-main)
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| Challenge: | Unlabeled data are useful for few-shot learning of language models. |
| Approach: | They propose a prompt-based few-shot learner that uses unlabeled data to fine-tune language models. |
| Outcome: | The proposed approach outperforms state-of-the-art models on six sentence classification and six sentence-pair classification benchmarking tasks. |