Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Campagna, G.; Foryciarz, Agata; Moradshahi, Mehrad; Lam, Monica S.

doi:10.48550/arxiv.2005.00891

Cited by 10 publications

(15 citation statements)

References 12 publications

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“…To address this issue, several methods Zhou and Small, 2019) have proposed for transferring prior knowledge of existing domains to new ones. On the other hand, Campagna et al (2020) proposed an abstract dialogue model that leverages the ontology and in-domain templates to generate a large amount of synthesized data for domain adaptation. Different from their method, in this paper, we utilize a pre-trained seq2seq model and slot descriptions for cross-domain DST without any in-domain data.…”

Section: Related Workmentioning

confidence: 99%

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Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

Lin¹,

Liu²,

Moon³

et al. 2021

Preprint

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Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.In this paper, we propose a slot description enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes dialogue context and slots with a pre-trained self-attentive encoder, and generates slot values in an auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information across slots to facilitate cross-domain knowledge transfer. Experimental results on the MultiWOZ dataset show that our proposed method significantly improves existing stateof-the-art results in the zero-shot cross-domain setting.

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Section: Related Workmentioning

confidence: 99%

“…Note that the reported averaged zero shot joint goal accuracy is not comparable to multi-domains joint goal accuracy. *Result from(Campagna et al, 2020).…”

mentioning

confidence: 99%

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

Lin¹,

Liu²,

Moon³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Training a DST model often requires extensive annotated dialogue data. These data are often collected via a Wizard-of-Oz (Woz) (Kelley, 1984) setting, where two workers converse with each other and annotate the dialogue states of each utterance (Wen et al, 2017;Budzianowski et al, 2018;, or with a Machines Talking To Machines * Work done during internship at Facebook (M2M) framework (Shah et al, 2018), where dialogues are synthesized via the system and user simulators (Campagna et al, 2020;Lin et al, 2021b). However, both of these approaches have inherent challenges when scaling to large datasets.…”

Section: Introductionmentioning

confidence: 99%

“… adapt a copy mechanism for transferring prior knowledge of existing domains to new ones, whileZhou and Small (2019) use the ontology graph to facilitate domain knowledge transfer Campagna et al (2020). leverage the ontology and in-domain templates to generate a large amount of synthesized data for domain adaptation, and apply schema descriptions for tracking out-of-domain slots.…”

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confidence: 99%

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Zero-Shot Dialogue State Tracking via Cross-Task Transfer

Lin¹,

Liu²,

Madotto³

et al. 2021

Preprint

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Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the crosstask knowledge from general question answering (QA) corpora for the zero-shot DST task. Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multichoice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle "none" value slots in the zero-shot DST setting. The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz. Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.

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Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

Moradshahi¹,

Campagna²,

J.³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Self Cite

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We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a fewshot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators.We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains. Our models achieve an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares favorably to 69% and 80% obtained for English parser trained on gold English data and a few examples from validation set. We show our approach outperforms the previous state-of-theart methodology by more than 30% for hotels and 40% for restaurants with localized ontologies for the subset of languages tested.Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours. Our code is released open-source. 1

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Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Cited by 10 publications

References 12 publications

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

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