Papers with multi-task problem

3 papers
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)

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Challenge: Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks.
Approach: They propose a multi-task neural model to jointly extract entities and negations from medical reports.
Outcome: The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.
Language Modelling as a Multi-Task Problem (2021.eacl-main)

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Challenge: Using multitask learning, humans are optimising their behaviour towards a multitude of objectives to reach their goals in dayto-day life.
Approach: They propose to study language modelling as a multi-task problem by examining the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items.
Outcome: The proposed model is able to learn the linguistic concept of Negative Polarity Items (NPIs) and is a multi-task learning model.
Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean (2024.lrec-main)

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Challenge: Existing studies on MT evaluation characterize quality of output with a single number . a recent advancement in MT technologies has enabled higher-quality, more nuanced translations .
Approach: They propose a 1200-sentence MQM evaluation benchmark for English-Korean and a reference-free QE setup to evaluate the quality of the translations.
Outcome: The proposed model outperforms the existing model in style and accuracy.

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