TAMLEC uses taxonomy-aware tasks and parallel feature sharing in a transformer to improve extreme multi-label completion and few-shot label prediction.
Multi-Task Learning of Keyphrase Boundary Classification
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abstract
Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.
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Extreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
TAMLEC uses taxonomy-aware tasks and parallel feature sharing in a transformer to improve extreme multi-label completion and few-shot label prediction.