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],[],[],[],{"_87":303,"_124":303,"_125":304,"_127":128},{"_87":300,"_124":301,"_125":302,"_127":128},{"_87":298,"_124":298,"_125":299,"_127":128},{"_87":294,"_124":295,"_125":296,"_127":128},{"_87":290,"_124":291,"_125":292,"_127":128},{"_87":287,"_124":287,"_125":288,"_127":128},{"_87":285,"_124":285,"_125":286,"_127":128},{"_87":283,"_124":283,"_125":284,"_127":128},"PyTorchLightning",{"_129":128,"_124":283},"NLP",{"_129":128,"_124":285},"NeMo",{"_129":289,"_124":287},"framework","Natural Language Understanding","uscs_natural_language_understanding",{"_129":293,"_124":291},"application","DL","soln_dl",{"_129":297,"_124":295},"technology","Dialog State Tracking",{"_129":128,"_124":298},"Conversational AI","soln_conversational_ai",{"_129":297,"_124":301},"Bertbasecased",{"_129":128,"_124":303},{"_168":1278,"_170":1279},"ngcApps",[1231],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1212":1213,"_1214":1215,"_1216":1217,"_1218":1219,"_1220":316,"_199":342,"_1221":1222,"_1223":50,"_1224":145,"_1225":145,"_1226":1227,"_1228":145,"_1229":1230},"amplitude",{"_1209":1210,"_854":-7,"_1211":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1203":1204,"_1205":1206,"_1207":1208},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787562043234,"1.12.0","defaultSearchSuggestions",{"_1094":422,"_1095":1096,"_1097":1098,"_1099":1100},"announcements",[],"consentState",{"_380":1084,"_1085":145,"_1086":26,"_1087":26,"_1088":1089},"consentConfig",{"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381},"showContactAdmin",[],[],[355,356,357,358,359,360],{"_39":54,"_41":63,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":361,"_49":50},5,"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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","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635],[582,583,584,585,586],{"_420":496,"_87":496,"_86":422,"_394":606},{"_420":470,"_87":470,"_86":422,"_394":601},{"_420":596,"_87":596,"_86":422,"_394":597},{"_420":591,"_87":591,"_86":422,"_394":592},{"_420":587,"_87":587,"_86":422,"_394":588},"nvidia.partners",[589],{"_254":590,"_427":587,"_428":26,"_86":464},"edge","app.hushly.com",[593],{"_254":594,"_427":591,"_428":26,"_86":595},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[598],{"_254":599,"_427":596,"_428":26,"_86":600},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722],[674,675,676,677],{"_420":689,"_87":689,"_86":422,"_394":690},{"_420":684,"_87":684,"_86":422,"_394":685},{"_420":483,"_87":483,"_86":422,"_394":681},{"_420":596,"_87":596,"_86":422,"_394":678},[679],{"_254":680,"_427":596,"_428":145,"_86":600},"_gh_sess",[682],{"_254":683,"_427":483,"_428":145,"_86":490},"YSC","bizible.com",[686],{"_254":687,"_427":684,"_428":26,"_86":688},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[691],{"_254":692,"_427":689,"_428":26,"_86":693},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_254":764,"_427":542,"_428":26,"_86":765},{"_254":749,"_427":763,"_428":145,"_86":750},{"_254":732,"_427":545,"_428":26,"_86":657},{"_254":732,"_427":645,"_428":26,"_86":657},{"_254":761,"_427":542,"_428":26,"_86":762},{"_254":749,"_427":548,"_428":145,"_86":750},{"_254":759,"_427":542,"_428":26,"_86":760},{"_254":749,"_427":545,"_428":145,"_86":750},{"_254":757,"_427":644,"_428":26,"_86":758},{"_254":749,"_427":666,"_428":145,"_86":750},{"_254":729,"_427":545,"_428":26,"_86":731},{"_254":755,"_427":542,"_428":145,"_86":756},{"_254":753,"_427":542,"_428":26,"_86":754},{"_254":751,"_427":542,"_428":145,"_86":752},{"_254":749,"_427":640,"_428":145,"_86":750},{"_254":747,"_427":542,"_428":26,"_86":748},{"_254":745,"_427":542,"_428":26,"_86":746},{"_254":743,"_427":542,"_428":145,"_86":744},{"_254":741,"_427":649,"_428":26,"_86":742},{"_254":729,"_427":640,"_428":26,"_86":731},{"_254":739,"_427":542,"_428":26,"_86":740},{"_254":737,"_427":542,"_428":145,"_86":738},{"_254":735,"_427":542,"_428":26,"_86":736},{"_254":733,"_427":542,"_428":26,"_86":734},{"_254":732,"_427":542,"_428":26,"_86":657},{"_254":729,"_427":730,"_428":26,"_86":731},{"_254":727,"_427":542,"_428":26,"_86":728},{"_254":725,"_427":542,"_428":145,"_86":726},{"_254":723,"_427":542,"_428":26,"_86":724},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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