Papers by Yueheng Sun
LightFormer: Light-weight Transformer Using SVD-based Weight Transfer and Parameter Sharing (2023.findings-acl)
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| Challenge: | Deploying Transformer networks on resource-constrained edge devices is challenging. |
| Approach: | They propose a low-rank factorization initialized by SVD-based weight transfer and parameter sharing to compress and accelerate Transformer networks. |
| Outcome: | The proposed method achieves similar performance to the baseline Transformer with 3.8 times and 1.8 times fewer parameters and achieves 2.3 times speedup and 1.5 times speed up respectively. |
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation (2022.emnlp-main)
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| Challenge: | Image-to-text tasks such as captioning and controllable image descriptions have received extensive attention for decades. |
| Approach: | They propose a new perspective for image-to-text to generate spatial descriptions by combining two objects in an image. |
| Outcome: | The proposed model is awe-inspiring and human-like, and the proposed end-to-end architecture is the better choice for their integration. |
Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)
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| Challenge: | Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles. |
| Approach: | They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data . |
| Outcome: | Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases . |
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)
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| Challenge: | Experimental results show that crowdsourced annotations are highly effective under supervised conditions. |
| Approach: | They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features. |
| Outcome: | The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations. |
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging (2021.emnlp-main)
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| Challenge: | Experimental results show that joint models of word segmentation and POS tagging can lead to better performance because they are closely related. |
| Approach: | They propose a domain adaption method for Chinese word segmentation and POS tagging that uses a simple metric to model the gaps between target and target domains. |
| Outcome: | The proposed method can gain significant performance improvements over baselines on a benchmark dataset. |
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations (2022.acl-long)
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| Challenge: | Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus. |
| Approach: | They propose to use crowdsourcing annotations to build a large-scale but quality-unguaranteed corpus for opinion expression identification in Chinese. |
| Outcome: | The proposed model can be trained with a synthetic expert and is highly consistent with the training and testing phase. |
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing studies focus on frame semantic parsing as a graph construction problem. |
| Approach: | They propose an end-to-end neural model to tackle frame semantic parsing jointly. |
| Outcome: | The proposed model is highly competitive and performs better than pipeline models on two benchmark datasets. |