2020
DOI: 10.1162/tacl_a_00333
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Task-Oriented Dialogue as Dataflow Synthesis

Abstract: We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SM… Show more

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Cited by 63 publications
(75 citation statements)
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“…As mentioned above, we show that our single model-based approach can accurately generate both the appropriate response as well as predict the correct API call at the right time. Earlier work by Andreas et al (2020) and Hosseini-Asl et al (2020) employs a similar modeling approach to predict dialog state in task-based dialogs, which can be seen as a precursor to our API call prediction strategy. The TicketTalk movie ticketing dataset was created using the self-dialog collection method (Krause et al, 2017;Moghe et al, 2018;Byrne et al, 2019) in which a paid crowd-sourced worker writes both sides of the dialog (i.e.…”
Section: Modular Vs End-to-end Architecturesmentioning
confidence: 99%
“…As mentioned above, we show that our single model-based approach can accurately generate both the appropriate response as well as predict the correct API call at the right time. Earlier work by Andreas et al (2020) and Hosseini-Asl et al (2020) employs a similar modeling approach to predict dialog state in task-based dialogs, which can be seen as a precursor to our API call prediction strategy. The TicketTalk movie ticketing dataset was created using the self-dialog collection method (Krause et al, 2017;Moghe et al, 2018;Byrne et al, 2019) in which a paid crowd-sourced worker writes both sides of the dialog (i.e.…”
Section: Modular Vs End-to-end Architecturesmentioning
confidence: 99%
“…Our method might be extended to task-oriented dialog in a number of domains, for example, SMCalFlow (Andreas et al, 2020), where data sparsity often poses a problem.…”
Section: Comparison To Lexical Substitutionmentioning
confidence: 99%
“…1 Furthermore, we assume that program prediction is local in that it does not require program fragments to be copied from the dialogue history (but may still depend on history in other ways). Several formalisms, including the typed references of Zettlemoyer and Collins (2009) and the meta-computation operators of Semantic Machines et al (2020), make it possible to produce local program annotations even for dialogues like the one depicted in Figure 2, which reuse past computations. We transformed the datasets in our experiments to use such metacomputation operators (see Appendix C).…”
Section: Preliminariesmentioning
confidence: 99%
“…To compare with prior work for SMCALFLOW (Semantic Machines et al, 2020) and TREEDST (Cheng et al, 2020), we replicated their setups. For SMCALFLOW, we predict plans always conditioning on the gold dialogue history for each utterance, but we consider any predicted plan wrong if the refer are correct flag is set to false.…”
Section: Evaluation Detailsmentioning
confidence: 99%