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A lot of recent research uses RL to "fine-tune" NLP models. A practical example would be Google's recently announced Machine Translation System (https://arxiv.o
by dennybritz 10y ago
A lot of recent research uses RL to "fine-tune" NLP models. A practical example would be Google's recently announced Machine Translation System (https://arxiv.org/abs/1609.08144 https://arxiv.org/abs/1609.08144). It uses RL to directly optimize BLEU scores on translated sentences.
You'll find similar applications in state-of-the art models for chatbots for example. Though I agree, "widely used" may be somewhat of an overstatement. But it's becoming more common.
On a side note, I actually think RL makes a lot of sense for many NLP problems and it would be super interesting to build a pure RL approach to language modeling or translation. Nobody has managed to do that quite yet.
- deleted 10y ago[deleted]
- orthoganol 10y agoI agree, I would love to see or even work on pure RL approaches in NLP. I would also like to see HMMs explored more, which I think also make a lot of sense for NLP, if you think of the problem as sequences of hidden states represented by semantic frames or other hand crafted language features producing phrases and sentences.