  Spoken Language Understanding (SLU) systems parse speech into semantic
structures like dialog acts and slots. This involves the use of an Automatic
Speech Recognizer (ASR) to transcribe speech into multiple text alternatives
(hypotheses). Transcription errors, common in ASRs, impact downstream SLU
performance negatively. Approaches to mitigate such errors involve using richer
information from the ASR, either in form of N-best hypotheses or word-lattices.
We hypothesize that transformer models learn better with a simpler utterance
representation using the concatenation of the N-best ASR alternatives, where
each alternative is separated by a special delimiter [SEP]. In our work, we
test our hypothesis by using concatenated N-best ASR alternatives as the input
to transformer encoder models, namely BERT and XLM-RoBERTa, and achieve
performance equivalent to the prior state-of-the-art model on DSTC2 dataset. We
also show that our approach significantly outperforms the prior
state-of-the-art when subjected to the low data regime. Additionally, this
methodology is accessible to users of third-party ASR APIs which do not provide
word-lattice information.
