CLS Speaker Series: Arianna Bisazza - Tuesday (15/05), 11:00, F1.15, SP 107
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Elia Bruni
May 12, 2018, 7:26:24 AM5/12/18
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Hi all,
I am happy to announce that our next invited speaker is Arianna Bisazza (Leiden University) next Tuesday, 15/05, at 11:00 in room F1.15.
Best,
Elia
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Hints of linguistic structure in neural models of language and translation.
The advent of efficiently trainable neural networks has led to striking improvements in the accuracy of next word prediction, machine translation and many other NLP tasks. It has also produced models that are much less interpretable. In particular, the role played by linguistic structure in sequence prediction and sequence-to-sequence models remains hard to gauge. What makes recurrent neural networks work so well for next word prediction? Do neural translation models learn to extract linguistic features from raw data and exploit them in any explicable way? In this talk I will give an overview of recent work, including my own, that aims at answering these questions. I will also present recent experiments on the importance of recurrency for capturing hierarchical structure with sequential models. Answering these questions is important to establish whether injecting linguistic knowledge into neural models is a promising research direction, and to understand how close we are to building intelligent systems that can truly understand and process human language.
I am happy to announce that our next invited speaker is Arianna Bisazza (Leiden University) next Tuesday, 15/05, at 11:00 in room F1.15.
Best,
Elia
---
Hints of linguistic structure in neural models of language and translation.
The advent of efficiently trainable neural networks has led to striking improvements in the accuracy of next word prediction, machine translation and many other NLP tasks. It has also produced models that are much less interpretable. In particular, the role played by linguistic structure in sequence prediction and sequence-to-sequence models remains hard to gauge. What makes recurrent neural networks work so well for next word prediction? Do neural translation models learn to extract linguistic features from raw data and exploit them in any explicable way? In this talk I will give an overview of recent work, including my own, that aims at answering these questions. I will also present recent experiments on the importance of recurrency for capturing hierarchical structure with sequential models. Answering these questions is important to establish whether injecting linguistic knowledge into neural models is a promising research direction, and to understand how close we are to building intelligent systems that can truly understand and process human language.
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