Interconnected Question Generation with Coreference Alignment and
Conversation Flow Modeling
Abstract
We study the problem of generating interconnected questions in question-answering style
conversations. Compared with previous works
which generate questions based on a single
sentence (or paragraph), this setting is different in two major aspects: (1) Questions
are highly conversational. Almost half of
them refer back to conversation history using
coreferences. (2) In a coherent conversation,
questions have smooth transitions between
turns. We propose an end-to-end neural model
with coreference alignment and conversation
flow modeling. The coreference alignment
modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history. The conversation flow modeling builds a coherent conversation by starting questioning on the first
few sentences in a text passage and smoothly
shifting the focus to later parts. Extensive experiments show that our system outperforms
several baselines and can generate highly conversational questions. The code implementation is released at https://github.com/
Evan-Gao/conversaional-QG