Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
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Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance.
We present FewRel 2. 0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances?
The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.
Ranked #1 on Named Entity Recognition on SciERC (using extra training data)
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
Ranked #1 on Question Answering on TACRED
We present a novel end-to-end neural model to extract entities and relations between them.
Ranked #3 on Relation Extraction on ACE 2004
In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.
Ranked #7 on Relation Extraction on SemEval-2010 Task 8
In this paper, we propose a novel model for relation classification at the sentence level from noisy data.
Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.
Ranked #13 on Relation Extraction on SemEval-2010 Task 8