This paper describes how to apply selfattention with relative positional encodings to the task of relation extraction. We propose to use the self-attention encoder layer together with an additional positionaware attention layer that takes into account positions of the query and the object in the sentence. The self-attention encoder also uses a custom implementation of relative positional encodings which allow each word in the sentence to take into account its left and right context. The evaluation of the model is done on the TACRED dataset. The proposed model relies only on attention (no recurrent or convolutional layers are used), while improving performance w.r.t. the previous state of the art.
REF first applied self-attention encoder to relation extraction task and achieved competitive results on TACRED dataset.
Position-aware Self-attention with Relative Positional Encodings for Slot Filling
ArXiv
ArXiv
Computer Science