NLU FAQ, best practices, and general troubleshooti ..
Use Mix.nlu to build a highly accurate, high quality custom natural language understanding (NLU) system quickly and easily, even if you have never worked with NLU before. Train the nlu model at any time and test it against practice sentences. Identify problem areas where intents overlap too closely, confidence levels need to be boosted, or additional entities need to be defined.
As one simple example, whether or not determiners should be tagged as part of entities, as discussed above, should be documented in the annotation guide. Nuance provides a tool called the Mix Testing Tool (MTT) for running a test set against a deployed NLU model and measuring the accuracy of the set on different metrics. In conversations you will also see sentences where people combine or modify entities using logical modifiers—and, or, or not. Another reason to use a more general intent is that once an intent is identified, you usually want to use this information to route your system to some procedure to handle the intent. Update and refactoring of Modify samples and Verify samples sections to reflect updates to the UI of the Develop tab samples view and changes in functionality.
Verify samples before training
In the next set of articles, we’ll discuss how to optimize your NLU using a NLU manager. A good rule of thumb is to use the term NLU if you’re just talking about a machine’s ability to understand what we say. You can use the sys_cs_topic_language table to ensure that the NLU model and intent are binded to the right Virtual Agent topic per language.
This text can also be converted into a speech format through text-to-speech services. Developing a model is an iterative process that includes multiple training passes. For example, you can retrain your model when you add or remove sample sentences, annotate samples, verify samples, include or exclude certain samples, and so on. When you change the training data, your model no longer reflects the most up-to-date data. As this happens, the model must be retrained to enable testing the changes, exposing errors and inconsistencies, and so on. Natural language processing works by taking unstructured data and converting it into a structured data format.
Locale values
For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. For reasons described below, artificial training data is a poor substitute for training data selected from production usage data. In short, prior to collecting usage data, it is simply impossible to know what the distribution of that usage data will be. In other words, the primary focus of an initial system built with artificial training data should not be accuracy per se, since there is no good way to measure accuracy without usage data. Instead, the primary focus should be the speed of getting a “good enough” NLU system into production, so that real accuracy testing on logged usage data can happen as quickly as possible.
Finally, you can use the batch testing tool to import a dataset (CSV) of utterances and expected intents, test it against your model, and view the anticipated accuracy percentage. If your training data is not in English you can also use a different variant of a language model which
is pre-trained in the language specific to your training data. For example, there are chinese (bert-base-chinese) and japanese (bert-base-japanese) variants of the BERT model. A full list of different variants of
these language models is available in the
official documentation of the Transformers library. The model will not predict any combination of intents for which examples are not explicitly given in training data.
Notation convention for NLU annotations
After migrating a model, you need to re-train and re-publish the https://www.globalcloudteam.com/, even if the model’s status says “Published”. Check to make sure that the sys_nlu_model records align with what’s in ml_solution and ml_capability_definition base table. Then go to the Virtual Agent topic, and then unbind and rebind the NLU model/intent, and click ‘Save’. Rather than calling out your catalog items individually, you can create a vocabulary source that points to the catalog table. You can then use an entity that references the vocabulary source each time you want to mention a catalog item. See this Virtual Agent Academy video for a demo video of how to do it and the results.
- Alternatively, you can select all samples on the current page by clicking the Select this page check box above the list of samples.
- The very general NLUs are designed to be fine-tuned, where the creator of the conversational assistant passes in specific tasks and phrases to the general NLU to make it better for their purpose.
- Choosing an NLU pipeline allows you to customize your model and finetune it on your dataset.
- With list entities, Mix will know the values to expect at runtime.
- If there is no existing trained model or your model is out of date, Mix.nlu will train a new model before proceeding with the automation.
When you have not yet selected samples, this will show 0 / total samples. Alternatively, you can select all samples on the current page by clicking the Select this page check box above the list of samples. Clicking the check box beside an individual selected sample deselects that sample. Verification of the sample data needs to be carried out for each language in the model, and for each intent.
Training a model that includes prebuilt domains
If Try also recognized entities, the new sample will be added as Annotation-assigned. Detailed information about any errors and warnings encountered during training is provided as a downloadable log file in CSV format. If only warnings are encountered, a warning log file is generated. If any errors are encountered, an error log file is generated describing errors and also any warnings. To choose a few samples on the present page, use the check boxes beside the samples to individually select the samples.
The T5 model is trained on various datasets for 17 different tasks which fall into 8 categories. Natural Language Understanding is a best-of-breed text analytics service that can be integrated into an existing data pipeline that supports 13 languages depending on the feature. NLU is hosted in Dallas, Washington, D.C., Frankfurt, and Sydney. This interpreter object contains all the trained NLU components, and it will be the main object that we’ll interact with.
Develop your model
For example, ask customers questions and capture their answers using Access Service Requests (ASRs) to fill out forms and qualify leads. If you do encounter issues, you can revert your skill to an earlier version of your interaction model. For details, see Use a previous version of the interaction model. You can review the results of an evaluation on the NLU Evaluation panel, and then closely examine the results for a specific evaluation.
No annotations appear in the Results area if the NLU engine cannot interpret the entities in your sample using your model. Only your client application can provide this information at runtime. Note that the Results area will not reflect any the changes you have made to intents and entities since the last time you trained the model.
Scope and context
To create an annotation set, see NLU Annotation Set REST API Reference. After your annotation set passes the evaluation, rerun the evaluation when you make changes to your interaction model to make sure that your changes don’t degrade the accuracy. There are a variety of tools at your disposal to test the accuracy of your NLU models. You can also test utterances in the Virtual Agent designer and see the intent discovered, confidence threshold, and any entities.
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