1. A Fast Unified Model for Parsing and Sentence Understanding
Compilation theory
A Fast Unified Model for Parsing and Sentence Understanding
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

2. Summary Problematic SPINN overview Shift-reduce parser
SPINN and TreeLSTM
Advantages of the SPINN
Conclusion
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

3. Problematic
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

4. SPINN overview Stack-augmented Parser-Interpreter Neural Network
Combines parsing and interpretation
Shift-reduce parser
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

5. Shift-reduce parser A = B + 3 Reduce Shift Step Parse Stack Look Ahead
Unscanned
Parser Action id
= B + 3
Shift 1 =
B + 3 2
id =
+ 3 3
id = id + 4
id = value
Reduce by value 5
id = sums
Reduce by sums 6
id = sums +
int 7
id = sums + int
eof 8
id = sums + value 9 10
assign
Done
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

6. SPINN
Designed to produce a vector representation of a sentence as its output
SPINN = Shift-reduce + TreeLong Short-Term Memory based on neural network
Neural network : improve encoding of sentences by using their structure
Sentence are not linear sequence
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

7. Advantages of the SPINN
Most words have multiple senses or meanings : Use the context (hybrid).
Supports batched computation
Speedup of up to 25x over other tree-structured models
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

8. Conclusion Recent (2016) Part of deep learning expansion
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018

9. Bibliography https://arxiv.org/abs/1603.06021
Antoine SOUSTELLE - Pierre RAINERO
02/12/2018