Name
Title | ||
|---|---|---|
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data |
Abstract | ||
|---|---|---|
Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of such model-based solutions has been challenging due to high latency, correctness issues, and high data needs. In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production. We describe a family of sampling and modeling techniques to attain production quality with light-weight neural network models using only a fraction of the data that would be necessary otherwise, and show a thorough comparison between each. Our results show that domain complexity dictates the appropriate approach to achieve high data efficiency. Finally, we distill the lessons from our experimental findings into a list of best practices for production-level NLG model development, and present them in a brief runbook. Importantly, the end products of all of the techniques are small sequence-to-sequence models (2Mb) that we can reliably deploy in production. |
Year | Venue | DocType |
|---|---|---|
2020 | COLING | Conference |
Volume | Citations | PageRank |
2020.coling-industry | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankit Arun | 1 | 0 | 1.01 |
| Soumya Batra | 2 | 0 | 1.01 |
| Vikas S. Bhardwaj | 3 | 8 | 2.86 |
| Ashwini Challa | 4 | 0 | 0.68 |
| Pinar Donmez | 5 | 0 | 1.01 |
| Peyman Heidari | 6 | 0 | 1.01 |
| Hakan Inan | 7 | 8 | 1.68 |
| Shashank Jain | 8 | 0 | 1.01 |
| Anuj Kumar | 9 | 19 | 11.09 |
| Shawn Mei | 10 | 0 | 0.34 |
| Karthik Mohan | 11 | 153 | 7.44 |
| M. White | 12 | 12 | 5.03 |