Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Table 2: Results (averaged over 10 runs). ⋆: best score per column. Bold: best score per section. -: does not apply as the method does not produce utterance embeddings.
['[EMPTY]', '[EMPTY]', '[EMPTY]', '(pre, post)', 'P @ [ITALIC] k= [ITALIC] v', 'P @ [ITALIC] k=10', 'R @ [ITALIC] k=10', 'F1 @ [ITALIC] k=10', 'Omega index ×100 | [ITALIC] Q|= [ITALIC] v', 'Omega index ×100 | [ITALIC] Q|=11']
Quantitative results. All variants of our model significantly outperform LD. While HAN is much stronger than LD, our model and its variants using best context sizes manage to outperform it everywhere, except in the siamese/P@k=v case (row j). One of the reasons for the superiority of our utterance encoder is probably that it considers contextual information while encoding the current utterance, while HAN and LD take as input the context utterances together with the current utterance, without distinguishing between them. Moreover, we use an attention mechanism dedicated to temporality, whereas HAN is only able to capture an implicit notion of time through the use of recurrence (RNN), and LD, with its dense layers, completely ignores it. Also, all variants of our model using best context sizes (11,11) outperform the ones using reduced (3,0) or no (0,0) context, regardless of the meta-architecture. This confirms the value added by our context encoder.