ConveRT: Efficient and Accurate Conversational Representations from Transformers github.com/PolyAI-LDN/polyai-models
Table 6: Intent classification results.
['[EMPTY]', 'Banking', 'Shopping', 'Company FAQ']
Intent Classification. The scores show very competitive performance of ConveRT encodings rx transferred to another dialog task. They outperform use-large in all three tasks and bert-large in 2/3 tasks. Note that, besides quicker pretraining, intent classifiers based on ConveRT encodings train 40 times faster than bert-large-based ones, as only the classification layers are trained for ConveRT. In sum, these preliminary results suggest that ConveRT as a sentence encoder can be useful beyond the core response selection task. The usefulness of ConveRT-based sentence representations have been recently confirmed on other intent classification datasets Casanueva et al. , with different intent classifiers Bunk et al. In future work, we plan to investigate other possible applications of transfer, especially for low-data setups.