Papers with biaffine attention model

2 papers
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (P18-2)

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Challenge: Existing methods for identifying and clustering mentions in text are complex and require heuristics to solve.
Approach: They propose to use a biaffine attention model to get antecedent scores for each possible mention and optimize mention detection and mention clustering accuracy given the mention cluster labels.
Outcome: The proposed model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set.
Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)

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Challenge: Existing methods for decoding dependency trees are 10 times faster than current ones.
Approach: They propose a graph-based method to tackle a dependency tree linearization task . they propose to solve a Traveling Salesman Problem and combine the solution into a projective tree .
Outcome: The proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.

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