Rationally Reappraising ATIS-based Dialogue Systems

The Air Travel Information Service (ATIS) corpus has been the most common benchmark for evaluating Spoken Language Understanding (SLU) tasks for more than three decades since it was released. Recent state-of-the-art neural models have obtained F1-scores near 98{\%} on the task of slot filling. We developed a rule-based grammar for the ATIS domain that achieves a 95.82{\%} F1-score on our evaluation set. In the process, we furthermore discovered numerous shortcomings in the ATIS corpus annotation, which we have fixed. This paper presents a detailed account of these shortcomings, our proposed repairs, our rule-based grammar and the neural slot-filling architectures associated with ATIS. We also rationally reappraise the motivations for choosing a neural architecture in view of this account. Fixing the annotation errors results in a relative error reduction of between 19.4 and 52{\%} across all architectures. We nevertheless argue that neural models must play a different role in ATIS dialogues because of the latter{'}s lack of variety.