Slot Self-Attentive Dialogue State Tracking

Ye, Fanghua; Manotumruksa, Jarana; Zhang, Qiang; Li, Shenghui; Yılmaz, Emine

doi:10.48550/arxiv.2101.09374

Cited by 6 publications

(7 citation statements)

References 52 publications

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“…Alternatively to encoding the full dialogue history, previous work has proposed including the state as context (Lei et al, 2018;Heck et al, 2020;Ye et al, 2021b) together with the last agent and user utterance. Recently, Cheng et al (2020) proposed replacing the agent utterance with a formal representation as well.…”

Section: Contextual Semantic Parsingmentioning

confidence: 99%

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Moradshahi,

Tsai,

Campagna

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one language, we can automatically produce an effective semantic parser for other languages using machine translation. We propose automatic translation of dialogue datasets with alignment to ensure faithful translation of slot values and eliminate costly human supervision used in previous benchmarks. We also propose a new contextual semantic parsing model, which encodes the formal slots and values, and only the last agent and user utterances. We show that the succinct representation reduces the compounding effect of translation errors, without harming the accuracy in practice.We evaluate our approach on several dialogue state tracking benchmarks. On RiSAWOZ, CrossWOZ, CrossWOZ-EN, and MultiWOZ-ZH datasets we improve the state of the art by 11%, 17%, 20%, and 0.3% in joint goal accuracy. We present a comprehensive error analysis for all three datasets showing erroneous annotations can lead to misguided judgments on the quality of the model. Finally, we present RiSAWOZ English and German datasets, created using our translation methodology. On these datasets, accuracy is within 11% of the original showing that high-accuracy multilingual dialogue datasets are possible without relying on expensive human annotations. We release our datasets and software open source. 1

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Section: Contextual Semantic Parsingmentioning

confidence: 99%

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Moradshahi,

Tsai,

Campagna

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…Alternatively to encoding the full dialogue history, previous work has proposed including the state as context (Lei et al, 2018;Heck et al, 2020;Ye et al, 2021b) together with the last agent and user utterance. Recently, Cheng et al (2020b) proposed replacing the agent utterance with a formal representation as well.…”

Section: Contextual Semantic Parsingmentioning

confidence: 99%

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Moradshahi¹,

Tsai²,

Campagna³

et al. 2021

Preprint

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Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one language, we can automatically produce an effective semantic parser for other languages using machine translation. We propose automatic translation of dialogue datasets with alignment to ensure faithful translation of slot values and eliminate costly human supervision used in previous benchmarks. We also propose a new contextual semantic parsing model, which encodes the formal slots and values, and only the last agent and user utterances. We show that the succinct representation reduces the compounding effect of translation errors, without harming the accuracy in practice.We evaluate our approach on several dialogue state tracking benchmarks. On RiSAWOZ, CrossWOZ, CrossWOZ-EN, and MultiWOZ-ZH datasets we improve the state of the art by 11%, 17%, 20%, and 0.3% in joint goal accuracy. We present a comprehensive error analysis for all three datasets showing erroneous annotations can obscure judgments on the quality of the model. Finally, we present RiSAWOZ English and German datasets, created using our translation methodology. On these datasets, accuracy is within 11% of the original showing that high-accuracy multilingual dialogue datasets are possible without relying on expensive human annotations.

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“…Recently, to further examine the generalization abilities, large scale cross-domain datasets have been proposed (Budzianowski et al, 2018;Zang et al, 2020;Eric et al, 2019;Rastogi et al, 2020b). Classification-based models (Ye et al, 2021; pick the candidate from the oracle list of possible slot values. The assumption of the full access of the schema makes them have limited generalization abilities.…”

Section: Multi-domain Dialogue State Trackingmentioning

confidence: 99%

“…There are two broad paradigms of DST models, classification-based and generation-based models, where the major difference is how the slot value is inferred. In classification-based models (Ye et al, 2021;, the prediction of a slot value is restricted to a fixed set for each slot, and non-categorical slots are constrained to values observed in the training data. In contrast, generationbased models decode slot values sequentially (token by token) based on the dialogue context, with the potential of recovering unseen values.…”

Section: Introductionmentioning

confidence: 99%

Dialogue State Tracking with a Language Model using Schema-Driven Prompting

Lee¹,

Cheng²,

Ostendorf³

2021

Preprint

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Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of indomain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: Multi-WOZ 2.1 and M2M. The data and code will be available at https://github.com/ chiahsuan156/DST-as-Prompting.

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Slot Self-Attentive Dialogue State Tracking

Cited by 6 publications

References 52 publications

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Dialogue State Tracking with a Language Model using Schema-Driven Prompting

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