jessestcharles

4 ms·

1. 1.▲
   by jessestcharles 8y ago

   One of the fantastic qualities of embedding based language models is that they provide a view on a semantic space that can be used quantitatively in most any downstream language task. As a conversational intelligence company Frame has many
2. 2.▲
   by jessestcharles 8y ago

   In this work we didn't explore classification performance characteristics. I suspect the nature of the misclassification at lower levels of domain data would revolve around the ways language usage differs in reviews vs common english.
3. 3.▲
   by jessestcharles 8y ago

   I think in this case the alignment between unlabeled domain data and the language task supports a convergence in language task performance. One argument to continue to prefer the ULM+domain model is that it is likely more generally capable
4. 4.▲
   by jessestcharles 8y ago

   Yes, great insight! The choice to focus on sentiment here was mainly to align with fast.ai’s original research—-hopefully maximizing the generalizability and accessibility of the results. Internally we have made use of these results to impr
5. 5.▲
   by jessestcharles 8y ago

   Author here! Happy to answer any questions that folks have.