Document-level relation extraction with Entity-Selection Attention

TL;DR: A Classifier-Adaptation Knowledge Distillation (CAKD) framework is proposed to address fundamental information extraction tasks, thus improving relation extraction and event detection performance and demonstrating the effectiveness of the proposed framework.

TL;DR: A novel document layout analysis framework with the Explicit Edge Embedding Network (E3 Net), which contains the edge embedding block and dynamic skip connection block to produce detailed features, as well as a lightweight fully convolutional subnet as the backbone for the effectiveness of the framework.

TL;DR: In this paper, a self-attention network is designed to generate the probability distribution of all patterns in a sentence, and then a probability distribution is applied as a constraint in the first Transformer layer to encourage its attention heads to follow the relational pattern structures.

TL;DR: Wang et al. as discussed by the authors proposed to explicitly model the heterogeneous affinity graph, HAG, including a mention graph (MG) and a coreference graph (CG), to capture the reasoning clues from the adjacent affinity matrix.

TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.

TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

TL;DR: This paper introduces a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units, and empirically shows that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.3 BLEU.

TL;DR: A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.