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.

翻译：一个裸语义表示可以通过多种方式用自然语言表达，具体取决于信息在表面层面的结构方式。我们旨在探索在从语义生成文本时控制话题焦点切分的方法。重点关注及物动词句子的主动语态与被动语态区分。其核心思想是在语义表示中添加话题等语用信息，从而在输入自然语言生成系统时强制生成主动或被动语态。由于图结构表示的语义中缺乏词语顺序的显式信息，我们采用图神经网络模型。针对意义到文本生成任务，我们尝试了三种基于图神经网络的不同话题焦点切分（TFA）方法。提出一种新颖的节点聚合编码策略：不同于传统通过聚合邻接节点信息进行编码的方式，该方法利用深度优先搜索来学习节点表示。实验结果表明，我们的方法在通用文本生成任务中能够达到与现有最优图模型相竞争的性能，同时在主动-被动语态转换任务上相比传统基于邻接关系的聚合策略取得显著改进。不同类型的话题焦点切分方式对图模型性能会产生巨大影响。