Compositional Generalization in Spoken Language Understanding
Avik Ray Yilin Shen Hongxia Jin
State-of-the-art spoken language understanding (SLU) models have shown
tremendous success in benchmark SLU datasets, yet they still fail in many
practical scenario due to the lack of model compositionality when trained on
limited training data. In this paper, we study two types of compositionality:
(a) novel slot combination, and (b) length generalization. We first conduct
in-depth analysis, and find that state-of-the-art SLU models often learn
spurious slot correlations during training, which leads to poor performance in
both compositional cases. To mitigate these limitations, we create the first
compositional splits of benchmark SLU datasets and we propose the first
compositional SLU model, including compositional loss and paired training that
tackle each compositional case respectively. On both benchmark and
compositional splits in ATIS and SNIPS, we show that our compositional SLU
model significantly outperforms (up to $5\%$ F1 score) state-of-the-art BERT
SLU model.