Neural Text Generation from Structured and Unstructured Data
Abstract
A number of researchers have recently questioned the necessity of increasingly complex
neural network (NN) architectures. In particular, several recent papers have shown that simpler,
properly tuned models are at least competitive across several natural language processing
tasks. In this thesis, we show that this is also the case for text generation from structured and
unstructured data. Specifically, we consider neural table-to-text generation and neural question
generation (NQG) tasks for text generation from structured and unstructured data respectively.
Table-to-text generation aims to generate a description based on a given table, and NQG is the
task of generating a question from a given passage where the generated question can be answered
by a certain sub-span of the passage using NN models. Experiments demonstrate that a basic
attention-based sequence-to-sequence model trained with exponential moving average technique
achieves state of the art in both tasks. We further investigate using reinforcement learning with
different reward functions to refine our pre-trained model for both tasks.
Collections
Cite this version of the work
Hamidreza Shahidi
(2019).
Neural Text Generation from Structured and Unstructured Data. UWSpace.
http://hdl.handle.net/10012/14979
Other formats