Papers by Ziran Li
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition (2020.coling-main)
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| Challenge: | Existing models cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. |
| Approach: | They propose a Heterogeneous User History graph convolution network which utilizes the user’s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two benchmark datasets and shows that it integrates user’s historical answers. |
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge (P19-1)
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| Challenge: | Existing methods for Chinese relation extraction suffer from segmentation errors and ambiguity of polysemy. |
| Approach: | They propose a multi-grained lattice framework for Chinese relation extraction . they incorporate word-level information into character sequence inputs to avoid segmentation errors . |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in distinct domains. |
Event Detection with Trigger-Aware Lattice Neural Network (D19-1)
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| Challenge: | Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese. |
| Approach: | They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two benchmark datasets and outperformed previous state- of-the art methods significantly. |
Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)
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| Challenge: | Existing approaches to vision-and-language pretraining (VLP) lack effectiveness and efficiency in downstream multimodal tasks. |
| Approach: | They propose a flexible vision-and-language pre-training model by incorporating cross-modal fusions into a dual-encoder architecture and a cross-module knowledge transfer strategy to guide the training process. |
| Outcome: | The proposed model is well-equipped with effectiveness and efficiency compared with other strong VLP models. |