To appear in ACL’16 Two Discourse Driven Language Models for Semantics
Table 6: Shallow discourse parsing results with frame-chain SemLM features. “FC” stands for the frame-chain SemLM. “TRI” is the tri-gram model while “LB” is the log-bilinear model. “pc”, “em” are conditional probability and frame embedding features, resp. “w/o DIS” indicates the case where we remove all discourse makers for SemLMs. We do the experiments by adding SemLM features to the base system. The improvement achieved by “FC-LB (pc+em)” over the baseline is statistically significant.
['[EMPTY]', 'CoNLL16 Test Explicit', 'CoNLL16 Test Implicit', 'CoNLL16 Test Overall', 'CoNLL16 Blind Explicit', 'CoNLL16 Blind Implicit', 'CoNLL16 Blind Overall']
Tri-gram with conditional probability features improve the performance for both explicit and implicit connective sense classifiers. Log-bilinear model with conditional probability features achieves even better results, and frame embeddings further improve the numbers. SemLMs improve relatively more on explicit connectives than on implicit ones.