A Latent Variable Recurrent Neural Networkfor Discourse Relation Language Models
Table 3: Language model perplexities (pplx), lower is better. The model dimensions K and H that gave best performance on the dev set are also shown.
['Model', 'PDTB  [ITALIC] K', 'PDTB  [ITALIC] H', 'PDTB pplx', 'SWDA  [ITALIC] K', 'SWDA  [ITALIC] H', 'SWDA pplx']
S5SS3SSS0Px1 Results The comparison between line 1 and line 2 shows the benefit of considering multi-sentence context information on language modeling. Line 3 shows that adding discourse relation information yields further improvements for both datasets. We emphasize that discourse relations in the test documents are marginalized out, so no annotations are required for the test set; the improvements are due to the disambiguating power of discourse relations in the training set.