MODELING INTER-SPEAKER RELATIONSHIP IN XLNET FOR CONTEXTUAL SPOKEN LANGUAGE UNDERSTANDING
Table 1: F1 score of state-of-the-art models and the proposed models on the test dataset and the validation dataset of DSTC 4 and Loqui.
['[BOLD] #', '[BOLD] Model', '[BOLD] DSTC 4 Test', '[BOLD] DSTC 4 Valid', '[BOLD] Loqui Test', '[BOLD] Loqui Valid']
XLNet-Base with both speaker-modeling methods (Speaker Symbol Token and Relative Speaker Attention) achieved the best F1 scores on the test and validation datasets of DSTC 4 (row 9). On DSTC 4 datasets, XLNet-Base with Speaker Symbol Token (row 7) was always better than XLNet-Base (row 6). When Relative Speaker Attention was added to row 7 (row 9), the model achieved the highest F1 scores on both test and validation datasets. However, adding Relative Speaker Attention did not always improve F1 scores (row 8).