Deep Learning and Formal Languages: Building Bridges

9:00-9:05 Opening Words.

9:05-9:45 Kevin Knight: Do Simpler Automata Learn Better?

9:45-9:51 Poster Spotlights:

• William Merrill: Sequential Neural Networks as Automata.
• Chunyang Xiao, Christoph Teichmann and Konstantine Arkoudas: Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing.
• Mathijs Mul and Willem Zuidema: Siamese recurrent networks can learn first-order logic reasoning and exhibit zero-shot generalization.

9:51-10:30 Ariadna Quattoni: A story about weighted automata (WFAs), RNNs and low-rank Hankel Matrices.

10:30-11:00 Break

11:00-11:40 Remi Eyraud: Distilling computational models from Recurrent Neural Networks.

11:40-11:45 – Poster Spotlights:

• Fabio Massimo Zanzotto, Giordano Cristini and Giorgio Satta: CYK Parsing over Distributed Representations.
• Farhana Ferdousi Liza and Marek Grzes: Relating RNN layers with the spectral WFA ranks in sequence modelling.

11:45-12:25 – John Kelleher: Using formal grammars to test ability of recurrent neural networks to model long-distance dependencies in sequential data.

12:25-12:30 – Poster Spotlights

• Abhijit Mahalunkar and John Kelleher: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies.
• Mirac Suzgun, Yonatan Belinkov, Stuart Shieber and Sebastian Gehrmann: LSTM Networks Can Perform Dynamic Counting.

12:30-14:00 Lunch

14:00-15:30 Poster Session

15:30-16:00 Break

16:00-16:40 Robert Frank: Beyond testing and acceptance: On the study of formal and natural languages in neural networks.

16:40-17:20 Noah Smith: Rational Recurrences for Empirical Natural Language Processing.

17:20-17:30 Closing Discussion.