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As far as I understand, it means that there must be a relation between a character's sentiment and what the next character can (/should) be for neural network t
by mdibaiee 10y ago
As far as I understand, it means that there must be a relation between a character's sentiment and what the next character can (/should) be for neural network to use this as a feature, am I right?
Does this mean we have unconsciously developed a language that exposes such relations?
- deleted 10y ago[deleted]
- vhold 10y agoThey muse about the reason behind the sentiment neuron in the paper. "It is an open question why our model recovers the concept of sentiment in such a precise, disentangled, interpretable, and manipulable way. It is possible that sentiment as a conditioning feature has strong predictive capability for language modelling. This is likely since sentiment is such an important component of a review." They go on to frame that as an important consideration for further work like this: "Our work highlights the sensitivity of learned representations to the data distribution they are trained on. The results make clear that it is unrealistic to expect a model trained on a corpus of books, where the two most common genres are Romance and Fantasy, to learn an encoding which preserves the exact sentiment of a review." I'm wondering if a "funniness" neuron could be discovered in a model trained on millions of jokes of various funniness, or what sorts of undiscovered meaning there is in other neurons in this model.