Papers by Zhengjue Wang

2 papers
Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)

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Challenge: Abstractive document summarization is a comprehensive task in natural language processing.
Approach: They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly .
Outcome: The proposed model is compatible with Transformer-based models and user-friendly.
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)

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Challenge: Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset.
Approach: They propose an autoencoding topic model with a mixture prior to perform clustering for the data.
Outcome: The proposed model can learn knowledge from different samples while extracting cluster-specific features.

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