Athul Paul Jacob


2018

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Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen | Zhouhan Lin | Athul Paul Jacob | Alessandro Sordoni | Aaron Courville | Yoshua Bengio
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.

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Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences
Athul Paul Jacob | Zhouhan Lin | Alessandro Sordoni | Yoshua Bengio
Proceedings of The Third Workshop on Representation Learning for NLP

We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurrent network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.