Zero-Shot Cross-Lingual Sequence Tagging as Seq2Seq Generation for Joint Intent Classification and Slot Filling
Fei Wang, Kuan-Hao Huang, Anoop Kumar, Aram Galstyan, Greg Ver Steeg, Kai-Wei Chang · 2022
The joint intent classification and slot filling task seeks to detect the intent of an utterance and extract its semantic concepts.In the zeroshot cross-lingual setting, a model is trained on a source language and then transferred to other target languages through multi-lingual representations without additional training data.While prior studies show that pre-trained multilingual sequence-to-sequence (Seq2Seq) models can facilitate zero-shot transfer, there is little understanding on how to design the output template for the joint prediction tasks.In this paper, we examine three aspects of the output template -(1) label mapping, (2) task dependency, and (3) word order.Experiments on the MASSIVE dataset consisting of 51 languages show that our output template significantly improves the performance of pretrained cross-lingual language models.