Learning Out-of-Vocabulary Words in Intelligent Personal Agents

By Avik Ray, Yilin Shen, Hongxia Jin. 2018

7 citations recorded by the source corpus.

Semantic parsers play a vital role in intelligent agents to convert natural language instructions to an actionable logical form representation. However, after deployment, these parsers suffer from poor accuracy on encountering out-of-vocabulary (OOV) words, or significant accuracy drop on previously supported instructions after retraining. Achieving both goals simultaneously is non-trivial. In this paper, we propose novel neural networks based parsers to learn OOV words; one incorporating a new hybrid paraphrase generation model, and an enhanced sequence-to-sequence model. Extensive experiments on both benchmark and custom datasets show our new parsers achieve significant accuracy gain on OOV words and phrases, and in the meanwhile learn OOV words while maintaining accuracy on previously supported instructions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangyu Zeng
2. Hongxia Jin
3. Yilin Shen
4. Yixuan Li
5. Zixuan Ke

In Computer Vision and Pattern Recognition

1. Hongxia Jin
2. Yilin Shen
3. Yixuan Li
4. Zixuan Ke

Citations in the corpus

• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “On the other hand, (Ray et al., 2018; Shen et al., 2018b) attempt to personalize the slot filling model.”