Papers by XieXiong Lin

2 papers
A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation (2022.findings-naacl)

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Challenge: Existing methods for title generation are based on timestep aware sentence embeddings, but they are not effective for generating a title with appropriate information in the content.
Approach: They propose a Timestep aware Sentence Embedding mechanism which refreshes the sentences’ embeddings with corresponding key words in different decoding timesteps.
Outcome: The proposed framework outperforms existing methods on various title generation tasks and the evaluation scores are significantly higher than previous approaches.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)

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Challenge: Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word.
Approach: They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text.
Outcome: The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks.

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