Abstract
In the literature, most of the previous studies
on English implicit discourse relation recognition only use sentence-level representations,
which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a
topic tensor network to recognize Chinese implicit discourse relations with both sentencelevel and topic-level representations. In particular, besides encoding arguments (discourse
units) using a gated convolutional network to
obtain sentence-level representations, we train
a simplified topic model to infer the latent
topic-level representations. Moreover, we feed
the two pairs of representations to two factored tensor networks, respectively, to capture
both the sentence-level interactions and topiclevel relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse
corpus, shows that our proposed model significantly outperforms several state-of-the-art
baselines in both micro and macro F1-scores