Papers by Athul Paul Jacob

2 papers
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)

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Challenge: Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error.
Approach: They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances.
Outcome: The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin.
Multitasking Inhibits Semantic Drift (2021.naacl-main)

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Challenge: Existing studies have found that LLP training is prone to semantic drift (use of messages inconsistent with their natural language meanings)
Approach: They propose to use latent language policies to train neural LLPs to eliminate semantic drift in a well-studied family of signaling games to reduce drift and improve sample efficiency.
Outcome: The proposed model eliminates semantic drift in a well-studied family of signaling games while improving sample efficiency.

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