billpei/wikiqa

Experiments on Answer Triggering using WikiQA

Repo for Q-A and Answer triggering systems

This repository contains experiments on the WikiQA dataset mainly for investigating answer triggering - finding the relevant answer out of a set of given answers to a question.

The base CNN implementation is from Deep learning for answer sentence selection. The logic of using CNN for Sentence selection using word vectors is inspired from Yoon Kim's - CNN for Sentence Classification The dataset is read from data/ folder and currently hardcoded in the last-line of the text. Uses keras for CNN.

Model

The current implementation maps the question and answer vectors into a higher dimension using CNN and compares them using dot product or a fully connected layer.

CNN statistics

Parameter	Value
Filter Size	2,3,4
Convolution	Levels
Word vector	dimensions
Dense Layer	Dimensions
Pooling	Max Pooling or Average Pooling

CNN Architecture

Below diagram shows the cnn-architecture taken from Deep learning for answer sentence selection but involved max pooling also alongwith filters of dimension 3 and 4, in addition to dimension 2.

The output of CNN is connected to a Logistic Layer which classifies the answer to the question as relevant(1) or non-relevant(0)

Tweaks

Because the dataset is highly skewed( One true answer to 5-6 false answers to a question ), RandomOversampling is done to balance the set.