Dealing with negative samples with multi-task learning on span-based
  joint entity-relation extraction
Chenguang Xue Jiamin Lu
Recent span-based joint extraction models have demonstrated significant
advantages in both entity recognition and relation extraction. These models
treat text spans as candidate entities, and span pairs as candidate
relationship tuples, achieving state-of-the-art results on datasets like ADE.
However, these models encounter a significant number of non-entity spans or
irrelevant span pairs during the tasks, impairing model performance
significantly. To address this issue, this paper introduces a span-based
multitask entity-relation joint extraction model. This approach employs the
multitask learning to alleviate the impact of negative samples on entity and
relation classifiers. Additionally, we leverage the Intersection over
Union(IoU) concept to introduce the positional information into the entity
classifier, achieving a span boundary detection. Furthermore, by incorporating
the entity Logits predicted by the entity classifier into the embedded
representation of entity pairs, the semantic input for the relation classifier
is enriched. Experimental results demonstrate that our proposed SpERT.MT model
can effectively mitigate the adverse effects of excessive negative samples on
the model performance. Furthermore, the model demonstrated commendable F1
scores of 73.61\%, 53.72\%, and 83.72\% on three widely employed public
datasets, namely CoNLL04, SciERC, and ADE, respectively.