Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
This work addresses the challenge of scalable and robust spoken language understanding for slot-filling dialogues, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of semantic decoding in spoken dialogue systems by proposing a deep learning architecture that uses unaligned semantic annotations and distributed semantic representations to overcome limitations of delexicalisation, achieving results on the DSTC2 and In-car corpora with higher word error rates.
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).