The ATIS Spoken Language Systems Pilot Corpus

By Charles T. Hemphill, J. Godfrey, G. Doddington. 1990

Speech research has made tremendous progress in the past using the following paradigm:• define the research problem,• collect a corpus to objectively measure progress, and• solve the research problem.Natural language research, on the other hand, has typically progressed without the benefit of any corpus of data with which to test research hypotheses. We describe the Air Travel Information System (ATIS) pilot corpus, a corpus designed to measure progress in Spoken Language Systems that include both a speech and natural language component. This pilot marks the first full-scale attempt to collect such a corpus and provides guidelines for future efforts.

Subfields: Natural Language Processing

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Citations in the corpus

Calibrate Before Use: Improving Few-Shot Performance of Language Models (2021, methodology): “MIT-G, MIT-D, ATIS-A, and ATIS-D indicate the MIT Genre, MIT Director, ATIS Airline, and ATIS Departure Date datasets.”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “The ATIS (Air Travel Information System) Pilot Corpus (Hemphill et al., 1990) is one of the first human-machine corpora.”
Lifelong Intent Detection via Multi-Strategy Rebalancing (2021, methodology): “Intent Detection (ID) aims to accurately understand the user intent from a user utterance to guide downstream dialogue policy decisions [5, 10, 25].”
A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, methodology): “Dataset: We evaluate ProgModel on the following two benchmark datasets: ATIS (Airline Travel Information Systems) dataset (Hemphill et al., 1990): a widely used dataset in SLU research.”
Towards Open Intent Discovery for Conversational Text (2019, methodology): “Moreover, commonly used intent-labeled datasets in dialog research such as SNIPS [6] or ATIS [7, 17] largely have concise, coherent and single-sentence texts.”
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (2018, methodology): “One of the earliest dataset collected in this fashion is the ATIS corpus (Hemphill et al., 1990), where conversations between a client and an airline help-desk operator were recorded.”
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset (2019, methodology): “Initial datasets were limited to one domain, such as ATIS (Hemphill, Godfrey, and Doddington 1990) for spoken language understanding for ﬂights.”
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling (2016, methodology): “ATIS (Airline Travel Information Systems) data set [18] is widely used in SLU research.”
Key-Value Retrieval Networks for Task-Oriented Dialogue (2017, background): “Several classical corpora have consisted of moderately-sized collections of dialogues re-lated to travel-booking (Hemphill et al., 1990; Bennett and Rudnicky, 2002).”
Expanding the Scope of the ATIS Task: The ATIS-3 Corpus (1994, methodology): “The ATIS task was first used as a common ARPA spoken language evaluation task in 1990 [1,2].”
From Eliza to XiaoIce: challenges and opportunities with social chatbots (2018, background): “Since the 1990s, a lot of research has been done on task-completion conversational systems (Price, 1990; Hemphill et al., 1990; Dahl et al., 1994; Walk et al., 2001).”
Spoken language understanding using long short-term memory neural networks (2014, methodology): “We evaluated the standard LSTM and our extensions on the ATIS database [16] [23] [40].”
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (2019, methodology): “One is the publicly ATIS dataset (Hemphill et al., 1990) containing audio recordings of ﬂight reservations, and the other is the custom-intent-engines collected by Snips (SNIPS dataset) (Coucke et al., 2018).”
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (2019, background): “We also report performance on ATIS (Hemphill et al., 1990; Dahl et al., 1994a) for direct comparison to Suhr et al.”
Photon: A Robust Cross-Domain Text-to-SQL System (2020, background): “…et al., 2003; Li and Jagadish, 2014) democratize end user access to relational data and have attracted signiﬁcant research attention for decades (Hemphill et al., 1990; Dahl et al., 1994; Zelle and Mooney, 1996; Popescu et al., 2003; Bertomeu et al., 2006; Zhong et al., 2017; Yu et al., 2018,…”
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020, methodology): “This is because ATIS is severely imbalanced where one intent accounts for 96% of the entire data.”
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020, methodology): “This technique has been employed to construct several datasets (Hemphill et al., 1990; Wen et al., 2017; Yu et al., 2019).”