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Athul Paul Jacob

Alumni

Publications

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen
Zhouhan Lin
Athul Paul Jacob
Alessandro Sordoni
Aaron Courville
Yoshua Bengio
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances,… (voir plus) for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.
2021-12-31
Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences
Athul Paul Jacob
Zhouhan Lin
Alessandro Sordoni
Yoshua Bengio
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 recurren… (voir plus)t 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.
2018-06-30
Rep4NLP@ACL (publié)
doi.org