SQuAD: The Stanford Question Answering Dataset
Update #3: Convolutional model
August 2nd, 2016
Overview
- Model
- Implementation
- Next steps
Model
Key idea
- Convolutional neural network model for reranking pairs of short texts (query-doc, question-answer)
-
2 submodels:
- Learn optimal vector representation of Q-D
- Learn a similarity function between Q-D vectors
Paper: Severyn, Aliaksei, and Alessandro Moschitti. "Learning to rank short text pairs with convolutional deep neural networks." Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015.
Problem formulation
q_i \in Q
D_i=\{ d_{i1}, d_{i2},..., d_{in}\}
J_i=\{ y_{i1}, y_{i2},..., y_{in}\}
Candidate documents
Queries
Relevancy judgements
Given:
h(w, \psi(q_i, D_i)) -> R
Learn:
such that relevant sentences appear first
Binary classifier:
h(w, \psi(q_i, d_{ij})) -> y_{ij}
1. Learn representation of Q-D
1
1. Learn representation of Q-D
For each sentence we have:
Skipgram for Wikipedia dump+AQUAINT corpus, dim=50, window=5, freq>=5
Max pooling
Wide, RELU
Vector x
fed into
2
m=5
100 fiters
2. Learn similarity between Q-D
x^T_q*M*x_d
x_{join}
P(Y=j | x_{join})
Nonlinear func
2
R(q,d)
Training details
- Minimize cross-entropy loss function
- Parameters
- SGD with backpropagation
- Regularization to mitigate overfitting
- Data: TREC (answer sentence selection, microblog retrieval)
- MRR and MAP to evaluate the models
\theta= \{F_q, b_q, F_d, b_d; M; w_h, b_h, w_s, b_s \}
Implementation
Model for SQUAD's data
SQUAD's Questions
q_i \in Q
D_i=\{ d_{i1}, d_{i2},..., d_{in}\}
Sentences in paragraphs
J_i=\{ y_{i1}, y_{i2},..., y_{in}\}
We can use Jaccard/PMI
Candidate answers
Queries
Relevancy judgements
Our implementation
Embeddings
Additional features
Word (Glove/hybrid) or SE trained with SQUAD's data
Topic information
Next steps
3. Convolutional networks model
By Sophie Germain
3. Convolutional networks model
Carnegie Mellon University
