{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:23:57Z","timestamp":1771651437355,"version":"3.50.1"},"reference-count":72,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: Dialogue state tracker (DST) and response generator (RG). The dialogue state consists of the domain-slot-value triples, which are regarded as the user\u2019s constraints to search the domain-related databases. The large-scale task-oriented dialogue data with the annotated structured dialogue state usually are inaccessible. It prevents the development of the pretrained language model for the task-oriented dialogue. We propose a simple yet effective pretraining method to alleviate this problem, which consists of two pretraining phases. The first phase is to pretrain on large-scale contextual text data, where the structured information of the text is extracted by the information extracting tool. To bridge the gap between the pretraining method and downstream tasks, we design two pretraining tasks: ontology-like triple recovery and next-text generation, which simulates the DST and RG, respectively. The second phase is to fine-tune the pretrained model on the TOD data. The experimental results show that our proposed method achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.<\/jats:p>","DOI":"10.1162\/tacl_a_00534","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T16:26:42Z","timestamp":1674577602000},"page":"68-84","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":9,"title":["OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue"],"prefix":"10.1162","volume":"11","author":[{"given":"Zhi","family":"Chen","sequence":"first","affiliation":[{"name":"X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems, Beijing, China. zhenchi713@sjtu.edu.cn"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuncong","family":"Liu","sequence":"additional","affiliation":[{"name":"X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems, Beijing, China. chenlusz@sjtu.edu.cn"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su","family":"Zhu","sequence":"additional","affiliation":[{"name":"AISpeech Co., Ltd., Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyue","family":"Wu","sequence":"additional","affiliation":[{"name":"X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Yu","sequence":"additional","affiliation":[{"name":"X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems, Beijing, China. kai.yu@sjtu.edu.cn"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"2023012416103292700_bib1","article-title":"Towards a human-like open-domain chatbot","author":"Adiwardana","year":"2020","journal-title":"arXiv preprint arXiv:2001.09977"},{"key":"2023012416103292700_bib2","doi-asserted-by":"publisher","first-page":"344","DOI":"10.3115\/v1\/P15-1034","article-title":"Leveraging linguistic structure for open domain information extraction","volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Angeli","year":"2015"},{"key":"2023012416103292700_bib3","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/ASRU46091.2019.9003911","article-title":"Scalable neural dialogue state tracking","volume-title":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","author":"Balaraman","year":"2019"},{"key":"2023012416103292700_bib4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.18653\/v1\/2020.acl-main.9","article-title":"PLATO: Pre-trained dialogue generation model with discrete latent variable","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Bao","year":"2020"},{"key":"2023012416103292700_bib5","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D18-1547","article-title":"MultiWOZ - a large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Budzianowski","year":"2018"},{"key":"2023012416103292700_bib6","doi-asserted-by":"publisher","first-page":"4516","DOI":"10.18653\/v1\/D19-1459","article-title":"Taskmaster-1: Toward a realistic and diverse dialog dataset","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Byrne","year":"2019"},{"key":"2023012416103292700_bib7","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1109\/ICASSP.2018.8462272","article-title":"Policy adaptation for deep reinforcement learning-based dialogue management","volume-title":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Chen","year":"2018"},{"issue":"9","key":"2023012416103292700_bib8","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1109\/TASLP.2019.2919872","article-title":"AgentGraph: Toward universal dialogue management with structured deep reinforcement learning","volume":"27","author":"Chen","year":"2019","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"2023012416103292700_bib9","first-page":"7521","article-title":"Schema-guided multi-domain dialogue state tracking with graph attention neural networks","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Chen","year":"2020"},{"key":"2023012416103292700_bib10","doi-asserted-by":"publisher","first-page":"2400","DOI":"10.1109\/TASLP.2020.3013392","article-title":"Distributed structured actor-critic reinforcement learning for universal dialogue management","volume":"28","author":"Chen","year":"2020","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"2023012416103292700_bib11","article-title":"Credit: Coarse-to-fine sequence generation for dialogue state tracking","author":"Chen","year":"2020","journal-title":"arXiv preprint arXiv:2009.10435"},{"key":"2023012416103292700_bib12","article-title":"Preview, attend and review: Schema-aware curriculum learning for multi-domain dialog state tracking","author":"Dai","year":"2021","journal-title":"arXiv preprint arXiv:2106.00291"},{"key":"2023012416103292700_bib13","article-title":"MultiWOZ 2.1: Multi-domain dialogue state corrections and state tracking baselines","author":"Eric","year":"2019","journal-title":"arXiv preprint arXiv:1907 .01669"},{"key":"2023012416103292700_bib14","first-page":"37","article-title":"Key-value retrieval networks for task-oriented dialogue","volume-title":"Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue","author":"Eric","year":"2017"},{"key":"2023012416103292700_bib15","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-1863","article-title":"Hyst: A hybrid approach for flexible and accurate dialogue state tracking","author":"Goel","year":"2019","journal-title":"arXiv preprint arXiv:1907.00883"},{"key":"2023012416103292700_bib16","doi-asserted-by":"crossref","first-page":"583","DOI":"10.18653\/v1\/2020.acl-main.54","article-title":"End-to-end neural pipeline for goal-oriented dialogue systems using GPT-2","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Ham","year":"2020"},{"key":"2023012416103292700_bib17","article-title":"GALAXY: A generative pretrained model for task-oriented dialog with semi-supervised learning and explicit policy injection","author":"He","year":"2021","journal-title":"arXiv preprint arXiv:2111.14592"},{"key":"2023012416103292700_bib18","doi-asserted-by":"crossref","first-page":"35","DOI":"10.18653\/v1\/2020.sigdial-1.4","article-title":"TripPy: A Triple copy strategy for value independent neural dialog state tracking","volume-title":"Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue","author":"Heck","year":"2020"},{"key":"2023012416103292700_bib19","article-title":"A simple language model for task-oriented dialogue","author":"Hosseini-Asl","year":"2020","journal-title":"arXiv preprint arXiv:2005 .00796"},{"key":"2023012416103292700_bib20","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.18653\/v1\/P17-1167","article-title":"Search-based neural structured learning for sequential question answering","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Iyyer","year":"2017"},{"key":"2023012416103292700_bib21","article-title":"ConvBERT: Improving BERT with span-based dynamic convolution","volume":"33","author":"Jiang","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2023012416103292700_bib22","first-page":"4171","article-title":"BERT: Pretraining of deep bidirectional transformers for language understanding","volume-title":"Proceedings of NAACL-HLT","author":"Devlin","year":"2019"},{"key":"2023012416103292700_bib23","doi-asserted-by":"crossref","first-page":"567","DOI":"10.18653\/v1\/2020.acl-main.53","article-title":"Efficient dialogue state tracking by selectively overwriting memory","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Kim","year":"2020"},{"key":"2023012416103292700_bib24","first-page":"388","article-title":"Statistical significance tests for machine translation evaluation","volume-title":"Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing","author":"Koehn","year":"2004"},{"key":"2023012416103292700_bib25","doi-asserted-by":"publisher","first-page":"3748","DOI":"10.18653\/v1\/2020.emnlp-main.306","article-title":"OpenIE6: Iterative grid labeling and coordination analysis for open information extraction","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Kolluru","year":"2020"},{"key":"2023012416103292700_bib26","doi-asserted-by":"crossref","first-page":"8034","DOI":"10.1109\/ICASSP40776.2020.9053975","article-title":"A simple but effective BERT model for dialog state tracking on resource-limited systems","volume-title":"ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Lai","year":"2020"},{"key":"2023012416103292700_bib27","article-title":"Non-autoregressive dialog state tracking","volume-title":"International Conference on Learning Representations","author":"Le","year":"2020"},{"key":"2023012416103292700_bib28","doi-asserted-by":"crossref","first-page":"5478","DOI":"10.18653\/v1\/P19-1546","article-title":"SUMBT: Slot-utterance matching for universal and scalable belief tracking","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Lee","year":"2019"},{"key":"2023012416103292700_bib29","doi-asserted-by":"crossref","first-page":"64","DOI":"10.18653\/v1\/P19-3011","article-title":"ConvLab: Multi-domain end-to-end dialog system platform","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations","author":"Lee","year":"2019"},{"key":"2023012416103292700_bib30","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.18653\/v1\/P18-1133","article-title":"Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Lei","year":"2018"},{"key":"2023012416103292700_bib31","doi-asserted-by":"publisher","first-page":"7871","DOI":"10.18653\/v1\/2020.acl-main.703","article-title":"BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Lewis","year":"2020"},{"key":"2023012416103292700_bib32","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.18653\/v1\/D16-1127","article-title":"Deep reinforcement learning for dialogue generation","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","author":"Li","year":"2016"},{"key":"2023012416103292700_bib33","article-title":"CoCo: Controllable counterfactuals for evaluating dialogue state trackers","volume-title":"International Conference on Learning Representations","author":"Li","year":"2020"},{"key":"2023012416103292700_bib34","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.18653\/v1\/2020.emnlp-main.273","article-title":"MinTl: Minimalist transfer learning for task-oriented dialogue systems","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Lin","year":"2020"},{"key":"2023012416103292700_bib35","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00390","article-title":"Pretraining the noisy channel model for task-oriented dialogue","author":"Qi","year":"2021","journal-title":"arXiv preprint arXiv: 2103.10518"},{"key":"2023012416103292700_bib36","doi-asserted-by":"crossref","first-page":"3836","DOI":"10.18653\/v1\/P19-1373","article-title":"Pretraining methods for dialog context representation learning","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Mehri","year":"2019"},{"key":"2023012416103292700_bib37","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.18653\/v1\/P17-1163","article-title":"Neural belief tracker: Data-driven dialogue state tracking","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Mrk\u0161i\u0107","year":"2017"},{"key":"2023012416103292700_bib38","article-title":"Toward scalable neural dialogue state tracking","volume-title":"NeurIPS 2018, 2nd Conversational AI workshop","author":"Nouri","year":"2018"},{"key":"2023012416103292700_bib39","article-title":"SOLOIST: Few-shot task-oriented dialog with a single pre-trained auto-regressive model","author":"Peng","year":"2020","journal-title":"arXiv e-prints"},{"key":"2023012416103292700_bib40","doi-asserted-by":"publisher","first-page":"172","DOI":"10.18653\/v1\/2020.findings-emnlp.17","article-title":"Few-shot natural language generation for task-oriented dialog","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings","author":"Peng","year":"2020"},{"key":"2023012416103292700_bib41","article-title":"Teacher-student framework enhanced multi-domain dialogue generation","author":"Peng","year":"2019","journal-title":"arXiv preprint arXiv:1908.07137"},{"key":"2023012416103292700_bib42","doi-asserted-by":"publisher","first-page":"878","DOI":"10.3115\/v1\/P15-1085","article-title":"Language to code: Learning semantic parsers for if-this-then-that recipes","volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Quirk","year":"2015"},{"issue":"8","key":"2023012416103292700_bib43","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"issue":"140","key":"2023012416103292700_bib44","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"2023012416103292700_bib45","doi-asserted-by":"publisher","first-page":"8689","DOI":"10.1609\/aaai.v34i05.6394","article-title":"Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Rastogi","year":"2020"},{"key":"2023012416103292700_bib46","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.18653\/v1\/D19-1196","article-title":"Scalable and accurate dialogue state tracking via hierarchical sequence generation","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Ren","year":"2019"},{"key":"2023012416103292700_bib47","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.18653\/v1\/D18-1299","article-title":"Towards universal dialogue state tracking","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Ren","year":"2018"},{"key":"2023012416103292700_bib48","article-title":"Knowledge-aware language model pretraining","author":"Rosset","year":"2020","journal-title":"arXiv preprint arXiv:2007.00655"},{"key":"2023012416103292700_bib49","doi-asserted-by":"publisher","first-page":"5649","DOI":"10.18653\/v1\/2021.naacl-main.449","article-title":"Hierarchical transformer for task oriented dialog systems","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Santra","year":"2021"},{"key":"2023012416103292700_bib50","article-title":"Multi-task pre-training for plug-and-play task-oriented dialogue system","author":"Yixuan","year":"2021","journal-title":"arXiv preprint arXiv:2109.14739"},{"key":"2023012416103292700_bib51","doi-asserted-by":"publisher","first-page":"73","DOI":"10.18653\/v1\/P17-4013","article-title":"Pydial: A multi-domain statistical dialogue system toolkit","volume-title":"Proceedings of ACL 2017, System Demonstrations","author":"Ultes","year":"2017"},{"key":"2023012416103292700_bib52","doi-asserted-by":"publisher","first-page":"59","DOI":"10.18653\/v1\/W17-4508","article-title":"Tl; dr: Mining Reddit to learn automatic summarization","volume-title":"Proceedings of the Workshop on New Frontiers in Summarization","author":"V\u00f6lske","year":"2017"},{"issue":"11","key":"2023012416103292700_bib53","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1109\/TASLP.2018.2851664","article-title":"Sample efficient deep reinforcement learning for dialogue systems with large action spaces","volume":"26","author":"Weisz","year":"2018","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"2023012416103292700_bib54","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.18653\/v1\/D16-1233","article-title":"Conditional generation and snapshot learning in neural dialogue systems","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","author":"Wen","year":"2016"},{"key":"2023012416103292700_bib55","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.18653\/v1\/D15-1199","article-title":"Semantically conditioned LSTM-based natural language generation for spoken dialogue systems","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Wen","year":"2015"},{"key":"2023012416103292700_bib56","doi-asserted-by":"publisher","first-page":"38","DOI":"10.18653\/v1\/2020.emnlp-demos.6","article-title":"Transformers: State-of-the-art natural language processing","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Wolf","year":"2020"},{"key":"2023012416103292700_bib57","first-page":"917","article-title":"TOD-BERT: Pretrained natural language understanding for task-oriented dialogue","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Chien-Sheng","year":"2020"},{"key":"2023012416103292700_bib58","first-page":"808","article-title":"Transferable multi-domain state generator for task-oriented dialogue systems","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Chien-Sheng","year":"2019"},{"key":"2023012416103292700_bib59","first-page":"41","article-title":"Memory attention neural network for multi-domain dialogue state tracking","volume-title":"CCF International Conference on Natural Language Processing and Chinese Computing","author":"Zihan","year":"2020"},{"key":"2023012416103292700_bib60","doi-asserted-by":"publisher","first-page":"14230","DOI":"10.1609\/aaai.v35i16.17674","article-title":"UBAR: Towards fully end-to-end task-oriented dialog system with GPT-2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Yang","year":"2021"},{"key":"2023012416103292700_bib61","article-title":"SCoRe: Pre-training for context representation in conversational semantic parsing","volume-title":"International Conference on Learning Representations","author":"Tao","year":"2020"},{"key":"2023012416103292700_bib62","first-page":"154","article-title":"Find or classify? Dual strategy for slot-value predictions on multi-domain dialog state tracking","volume-title":"Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics","author":"Zhang","year":"2020"},{"key":"2023012416103292700_bib63","doi-asserted-by":"publisher","first-page":"5882","DOI":"10.18653\/v1\/2021.acl-long.457","article-title":"SMedBERT: A knowledge-enhanced pretrained language model with structured semantics for medical text mining","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Zhang","year":"2021"},{"key":"2023012416103292700_bib64","first-page":"9604","article-title":"Task-oriented dialog systems that consider multiple appropriate responses under the same context","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang","year":"2020"},{"key":"2023012416103292700_bib65","doi-asserted-by":"publisher","first-page":"270","DOI":"10.18653\/v1\/2020.acl-demos.30","article-title":"DIALOGPT: Large-scale generative pre-training for conversational response generation","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations","author":"Zhang","year":"2020"},{"key":"2023012416103292700_bib66","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.18653\/v1\/P19-1139","article-title":"ERNIE: Enhanced language representation with informative entities","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Zhang","year":"2019"},{"key":"2023012416103292700_bib67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18653\/v1\/W18-5001","article-title":"Zero-shot dialog generation with cross-domain latent actions","volume-title":"Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue","author":"Zhao","year":"2018"},{"key":"2023012416103292700_bib68","first-page":"1208","article-title":"Rethinking action spaces for reinforcement learning in end-to-end dialog agents with latent variable models","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Zhao","year":"2019"},{"key":"2023012416103292700_bib69","doi-asserted-by":"crossref","first-page":"654","DOI":"10.18653\/v1\/P17-1061","article-title":"Learning discourse-level diversity for neural dialog models using conditional variational autoencoders","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Zhao","year":"2017"},{"key":"2023012416103292700_bib70","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1135","article-title":"Global-locally self-attentive encoder for dialogue state tracking","volume-title":"ACL","author":"Zhong","year":"2018"},{"key":"2023012416103292700_bib71","doi-asserted-by":"crossref","first-page":"228","DOI":"10.18653\/v1\/2021.sigdial-1.24","article-title":"Dialogue state tracking with multi-level fusion of predicted dialogue states and conversations","volume-title":"Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue","author":"Zhou","year":"2021"},{"key":"2023012416103292700_bib72","article-title":"Multi-domain dialogue state tracking as dynamic knowledge graph enhanced question answering","author":"Li","year":"2019","journal-title":"arXiv preprint arXiv:1911.06192"}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00534\/2067879\/tacl_a_00534.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00534\/2067879\/tacl_a_00534.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T14:31:17Z","timestamp":1701786677000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00534\/114595\/OPAL-Ontology-Aware-Pretrained-Language-Model-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":72,"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00534","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023]]},"published":{"date-parts":[[2023]]}}}