Papers with multi-task frameworks

2 papers
A general framework for information extraction using dynamic span graphs (N19-1)

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Challenge: Existing frameworks for information extraction use a pipeline approach to identify entities and then use the detected entity spans for relation extraction and coreference resolution.
Approach: They propose a framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Outcome: The proposed framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains.
Cross-Lingual Abstractive Summarization with Limited Parallel Resources (2021.acl-long)

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Challenge: Existing approaches to cross-lingual summarization use limited available cross-linguistic resources.
Approach: They propose a multi-task framework for cross-lingual abstractive summarization that uses a single decoder to generate monolingual and cross-linguistic summaries.
Outcome: Experiments on two CLS datasets show that the proposed model outperforms baseline models in low-resource and full-dataset scenarios.

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