Papers by Lianzhe Huang
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models. |
| Approach: | They propose a model that uses a unified prompt for all languages, called UniPrompt, to alleviate the effort of designing different prompts for multiple languages. |
| Outcome: | The proposed model outperforms baseline models in the zero-shot cross-lingual setting. |
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
| Approach: | They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. |
| Outcome: | The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge. |
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text. |
| Approach: | They propose to embed hierarchy into a text encoder by combining input and output data to generate a hierarchy-aware representation. |
| Outcome: | Extensive experiments on three benchmark datasets verify the effectiveness of the proposed model. |
Text Level Graph Neural Network for Text Classification (D19-1)
Copied to clipboard
| Challenge: | Recent researches have explored graph neural network (GNN) techniques on text classification, but they are faced with the problems of fixed corpus level graph structure which don’t support online testing and high memory consumption. |
| Approach: | They propose a graph neural network model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. |
| Outcome: | The proposed model outperforms existing models on several text classification datasets even with consuming less memory. |