Controlling Topic-Focus Articulation in Meaning-to-Text Generation using
  Graph Neural Networks
Chunliu Wang Rik van Noord Johan Bos
A bare meaning representation can be expressed in various ways using natural
language, depending on how the information is structured on the surface level.
We are interested in finding ways to control topic-focus articulation when
generating text from meaning. We focus on distinguishing active and passive
voice for sentences with transitive verbs. The idea is to add pragmatic
information such as topic to the meaning representation, thereby forcing either
active or passive voice when given to a natural language generation system. We
use graph neural models because there is no explicit information about word
order in a meaning represented by a graph. We try three different methods for
topic-focus articulation (TFA) employing graph neural models for a
meaning-to-text generation task. We propose a novel encoding strategy about
node aggregation in graph neural models, which instead of traditional encoding
by aggregating adjacent node information, learns node representations by using
depth-first search. The results show our approach can get competitive
performance with state-of-art graph models on general text generation, and lead
to significant improvements on the task of active-passive conversion compared
to traditional adjacency-based aggregation strategies. Different types of TFA
can have a huge impact on the performance of the graph models.