Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
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Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
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