Andre Cianflone


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Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers
Andre Cianflone | Yulan Feng | Jad Kabbara | Jackie Chi Kit Cheung
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.

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Attention for Implicit Discourse Relation Recognition
Andre Cianflone | Leila Kosseim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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N-gram and Neural Language Models for Discriminating Similar Languages
Andre Cianflone | Leila Kosseim
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45% with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45% which is close to the accuracy of 89.38% achieved by the best submission.

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The CLaC Discourse Parser at CoNLL-2016
Majid Laali | Andre Cianflone | Leila Kosseim
Proceedings of the CoNLL-16 shared task