Neural Enquirer: Learning to Query Tables with Natural Language

Goal

A neural network aka Neural Enquirer(NE) to execute a natural language query on knowledge-base for answer.

Model

vs End-to-End semantic parser NE is fully differentiable. model

Query Encoder

bi-direction RNN

Table Encoder

table embedding of element at m,n of the table is a one layer non-linearity for a matrix transformation given the contatination of the embedding element at corresponding index with the corresponding column field name embedding.

Executor

each executor is responsible for one type of operation(select, where, max etc.)

Query is executed as a cascade process of the executers.

####Memory

Memory layer is used to store the intermediate result of the executers with each annotator has access to previous temporal-level memory.

Reader

attention agnostic to each value in the row reader

Annotator

annotator

Train

N2N

train semantic parsing component

this paper is more like working towards using the table structure(compositional structure) through a N2N training to minimized the draw back in semantic representation of the query sentence embedding.

Experiments

Dataset: huawei’s own synthetic dataset.(not yet public available) comparision against SEMPRE: annotator

##My questions

how to optimize the speed if each query need to search the whole table with distributed representation?(aka scalability)