Model Overview

This is a cased dialogue state tracking model with a BERT [1] Base encoder finetuned on dataset Google SGD [2].\nThe model is based on the architecture presented in \"SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services\" paper [3].

Model Architecture

SGD-QA is a multi-pass NLU model consisting of a BERT [1] encoder with multi-task heads. We use a pre-trained BERT model\nwith four classification heads and one span prediction head for slot value extraction. The SGD-QA model relies on a question\nanswering approach and uses schema description\nas query and the dialogue turn as context. Each input instance is intended for exactly one task which\nis implemented by masking out the other task's losses.\nIntent prediction, requested slot prediction and categorical slot value prediction are formulated as\nbinary sequence classification. The slot status prediction task is a 3-way sequence classification to\npredict whether a slot is active, non-active (\"none\")\nor does not matter to the user (\"dontcare\"). Multiple slots can become active in a single turn. Noncategorical slot value extraction extracts the values\nfor non-categorical slots by using a span-based prediction head, similar to SQuAD, where two token classification layers detect\nthe start and end positions of the slot value in the\ndialogue turn. Like the categorical slot value prediction task, its prediction is only used when the\nslot status is active.

Training

The model is trained on the full dataset Google SGD [2]. We used NVIDIA DGX1 with 8 V100 GPUs.\nHyperparameters can be found in [3].

Dataset

The Google SGD dataset [2] is the biggest dataset\nfor goal-oriented dialogue state tracking with over 20k\nannotated dialogues for 45 services spanning 20\ndomains. The SGD dataset defines an ontology,\ncalled schema, that contains descriptions in\nnatural language for all entities associated with\na particular service. Slots are further classified\ninto non-categorical slots and categorical slots.\nFor categorical slots, the schema also includes a\nlist of possible values. The user-system dialogues\ncan be either single-domain or multi-domain,\nwhere a user can request two or more services per\ndialogue. Each turn is labeled with relevant schema\ninformation, called dialogue state, comprising\nof the active intent, requested user slots and slot\nassignments that occurred throughout the dialogue.\nThe SGD dataset is designed to test services\nbeyond those seen at training: 57% of the dev and\n78% of the test dataset stem from with unseen\nservices. Nevertheless, seen and unseen services\ncan share similar slots and functionality

Performance

The latest model in the version history has the following joint goal accuracy which measures the accuracy of\npredicting all slot assignments for a turn correctly.

Dev set

ALL\tSERVICES 59.72

SEEN SERVICES\t65.7

UNSEEN SERVICES 51.96

Test set

ALL\tSERVICES 45.85

SEEN SERVICES 49.44

UNSEEN SERVICES 44.65

How to Use this Model

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.dialogue_state_tracking.sgdqa_model.SGDQAModel.from_pretrained(model_name=\"sgdqa_bertbasecased\")\n

Test the model from NGC

python [NEMO_GIT_FOLDER]/examples/nlp/dialogue_state_tracking/sgd_qa.py do_training=false pretrained_model=sgdqa_bertbasecased model.dataset.data_dir=[EXISTING_DATA_DIR] model.dataset.dialogues_example_dir=[PATH_TO_PREPROCESSED_DATA] model.dataset.task_name=sgd_all trainer.gpus=1 model.test_ds.ds_item=[\"test\"]\n

If PATH_TO_PREPROCESSED_DATA does not exist, data will be preprocessed from EXISTING_DATA_DIR. If exists and user wants to load from cache for better performance set additionally model.dataset.use_cache=true

Input

The schema and dialogue json files in their original format

Output

dialogue state predictions during evaluation can be dumped as json files

Limitations

Since this model was trained on publically available datasets, the performance of this model might degrade for custom data that the model has not been trained on.

References

[1] https://arxiv.org/pdf/1810.04805.pdf

[2] https://github.com/google-research-datasets/dstc8-schema-guided-dialogue

[3] https://arxiv.org/abs/2105.08049

License

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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We use a pre-trained BERT model\nwith four classification heads and one span prediction head for slot value extraction. The SGD-QA model relies on a question\nanswering approach and uses schema description\nas query and the dialogue turn as context. Each input instance is intended for exactly one task which\nis implemented by masking out the other task's losses. \nIntent prediction, requested slot prediction and categorical slot value prediction are formulated as\nbinary sequence classification. The slot status prediction task is a 3-way sequence classification to\npredict whether a slot is active, non-active (\"none\")\nor does not matter to the user (\"dontcare\"). Multiple slots can become active in a single turn. Noncategorical slot value extraction extracts the values\nfor non-categorical slots by using a span-based prediction head, similar to SQuAD, where two token classification layers detect\nthe start and end positions of the slot value in the\ndialogue turn. Like the categorical slot value prediction task, its prediction is only used when the\nslot status is active.\n\n\n## Training\n \nThe model is trained on the full dataset Google SGD [2]. We used NVIDIA DGX1 with 8 V100 GPUs.\nHyperparameters can be found in [3].\n\n### Dataset\n\nThe Google SGD dataset [2] is the biggest dataset\nfor goal-oriented dialogue state tracking with over 20k\nannotated dialogues for 45 services spanning 20\ndomains. The SGD dataset defines an ontology,\ncalled schema, that contains descriptions in\nnatural language for all entities associated with\na particular service. Slots are further classified\ninto non-categorical slots and categorical slots.\nFor categorical slots, the schema also includes a\nlist of possible values. The user-system dialogues\ncan be either single-domain or multi-domain,\nwhere a user can request two or more services per\ndialogue. Each turn is labeled with relevant schema\ninformation, called dialogue state, comprising\nof the active intent, requested user slots and slot\nassignments that occurred throughout the dialogue.\nThe SGD dataset is designed to test services\nbeyond those seen at training: 57% of the dev and\n78% of the test dataset stem from with unseen\nservices. Nevertheless, seen and unseen services\ncan share similar slots and functionality\n\n## Performance \n\nThe latest model in the version history has the following joint goal accuracy which measures the accuracy of\npredicting all slot assignments for a turn correctly.\n\nDev set\n\nALL\tSERVICES 59.72\n\nSEEN SERVICES\t65.7\n\nUNSEEN SERVICES 51.96\n\nTest set\n\nALL\tSERVICES 45.85\n\nSEEN SERVICES 49.44\n\nUNSEEN SERVICES 44.65\n\n## How to Use this Model\n\n### Automatically load the model from NGC\n```python\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.dialogue_state_tracking.sgdqa_model.SGDQAModel.from_pretrained(model_name=\"sgdqa_bertbasecased\")\n```\n\n### Test the model from NGC\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/dialogue_state_tracking/sgd_qa.py do_training=false pretrained_model=sgdqa_bertbasecased model.dataset.data_dir=[EXISTING_DATA_DIR] model.dataset.dialogues_example_dir=[PATH_TO_PREPROCESSED_DATA] model.dataset.task_name=sgd_all trainer.gpus=1 model.test_ds.ds_item=[\"test\"]\n```\nIf `PATH_TO_PREPROCESSED_DATA` does not exist, data will be preprocessed from `EXISTING_DATA_DIR`. If exists and user wants to load from cache for better performance set additionally `model.dataset.use_cache=true`\n\n### Input \n\nThe schema and dialogue json files in their original format\n\n### Output\n\ndialogue state predictions during evaluation can be dumped as json files\n\n## Limitations\nSince this model was trained on publically available datasets, the performance of this model might degrade for custom data that the model has not been trained on.\n\n\n## References\n\n[1] https://arxiv.org/pdf/1810.04805.pdf\n\n[2] https://github.com/google-research-datasets/dstc8-schema-guided-dialogue\n\n[3] https://arxiv.org/abs/2105.08049\n\n\n## License\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","SGDQA Bertbasecased",["D",1680637331016],"/api/image/nvidia/nemo/models/sgdqa_bertbasecased","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[275,276,277,278,279,280,281,282],[],[],[],{"_91":303,"_128":303,"_129":304,"_131":132},{"_91":300,"_128":301,"_129":302,"_131":132},{"_91":298,"_128":298,"_129":299,"_131":132},{"_91":294,"_128":295,"_129":296,"_131":132},{"_91":290,"_128":291,"_129":292,"_131":132},{"_91":287,"_128":287,"_129":288,"_131":132},{"_91":285,"_128":285,"_129":286,"_131":132},{"_91":283,"_128":283,"_129":284,"_131":132},"PyTorchLightning",{"_133":132,"_128":283},"NLP",{"_133":132,"_128":285},"NeMo",{"_133":289,"_128":287},"framework","Natural Language Understanding","uscs_natural_language_understanding",{"_133":293,"_128":291},"application","DL","soln_dl",{"_133":297,"_128":295},"technology","Dialog State Tracking",{"_133":132,"_128":298},"Conversational AI","soln_conversational_ai",{"_133":297,"_128":301},"Bertbasecased",{"_133":132,"_128":303},{"_170":1475,"_172":1476},"ngcApps",[1428],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1409":1410,"_1411":1412,"_1413":1414,"_1415":1416,"_1417":316,"_201":340,"_1418":1419,"_1420":51,"_1421":149,"_1422":149,"_1423":1424,"_1425":149,"_1426":1427},"amplitude",{"_1406":1407,"_1012":-7,"_1408":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1400":1401,"_1402":1403,"_1404":1405},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1287":420,"_1288":1289,"_1290":1291,"_1292":1293},"announcements",[],"consentState",{"_377":1277,"_1278":149,"_1279":26,"_1280":26,"_1281":1282},"consentConfig",{"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378},"showContactAdmin",1783955688384,[],[],[354,355,356,357,358],{"_40":55,"_42":66,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":54,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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