An essential component of spoken language understanding (SLU) is slot
filling: representing the meaning of a spoken utterance using semantic entity
labels. In this paper, we develop end-to-end (E2E) spoken language
understanding systems that directly convert speech input to semantic entities
and investigate if these E2E SLU models can be trained solely on semantic
entity annotations without word-for-word transcripts. Training such models is
very useful as they can drastically reduce the cost of data collection. We
created two types of such speech-to-entities models, a CTC model and an
attention-based encoder-decoder model, by adapting models trained originally
for speech recognition. Given that our experiments involve speech input, these
systems need to recognize both the entity label and words representing the
entity value correctly. For our speech-to-entities experiments on the ATIS
corpus, both the CTC and attention models showed impressive ability to skip
non-entity words: there was little degradation when trained on just entities
versus full transcripts. We also explored the scenario where the entities are
in an order not necessarily related to spoken order in the utterance. With its
ability to do re-ordering, the attention model did remarkably well, achieving
only about 2% degradation in speech-to-bag-of-entities F1 score.

本文研究了直接将语音输入转换为语义实体的端到端口语理解系统，探索了这些端到端系统是否可以仅使用语义实体注释进行训练，并且使用注意力机制的编码器 - 解码器模型的效果最佳。