2311 00292 IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models

Entities or slots, are typically pieces of information that you want to capture from a users. In our previous example, we might have a user intent what is an embedded operating system of shop_for_item but want to capture what kind of item it is. There are many NLUs on the market, ranging from very task-specific to very general.

This can make it easier to develop and maintain your Mix.dialog application while taking advantage of the convenience of predefined entities. Consider an SMS messaging application, where samples include the destination phone number. There are billions of possible phone number combinations, so clearly you could not enumerate all the possibilities, nor would it really make sense to try. However, phone numbers would not be considered freeform input, since there is a fixed, systematic structure to phone numbers that falls under a small set of pattern formats. These patterns can be recognized either with a regex pattern (for typed in phone numbers) or a grammar (for spoken numbers).

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ATNs and their more general format called “generalized ATNs” continued to be used for a number of years. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently.

Hence the breadth and depth of “understanding” aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The “breadth” of a system is measured by the sizes of its vocabulary and grammar. The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding,[24] but they still have limited application. Systems that are both very broad and very deep are beyond the current state of the art.

How does Natural Language Understanding (NLU) work?

A new Expert organization role opens up permissions to access rule-based entity functionality in Mix. Previously this was only available to Nuance Professional Service users. Note how you do not simply annotate the literals “and” and “no” as an entity or tag modifier. Instead, tag modifiers are the parents of the annotations that they connect or negate.

• Resources are accessed via the NLUaaS gRPC API or the ASRaaS gRPC API.
• There are billions of possible phone number combinations, so clearly you could not enumerate all the possibilities, nor would it really make sense to try.
• You then link intents to functions or methods in your client application logic.
• At runtime the model tries to match user words with the regular expression.
• Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings.

Before the new entity is saved (or modified), Mix.nlu exports your existing NLU model to a ZIP file (one ZIP file per language) so that you have a backup of your NLU model. Creating (or modifying) a rule-based entity requires your NLU model to be retokenized, which may take some time and impact your existing annotations. Before the entity-type is created (or modified), Mix.nlu exports your existing NLU model to a ZIP file containing a TRSX file so that you have a backup. Creating (or modifying) a regex-based entity requires your NLU model to be re-tokenized, which may take some time and impact your existing annotations. When you add a value-literal pair, this pair will apply to the entity only in the currently selected language. The same value name can be used in multiple languages for the same list-based entity, but the value and its literals need to be added separately in each language.

About Mix.nlu

Some data management is helpful here to segregate the test data from the training and test data, and from the model development process in general. Ideally, the person handling the splitting of the data into train/validate/test and the testing of the final model should be someone outside the team developing the model. This section provides best practices around generating test sets and evaluating NLU accuracy at a dataset and intent level.. Mix includes a number of predefined entities; see predefined entities. By participating together, your group will develop a shared knowledge, language, and mindset to tackle challenges ahead. We can advise you on the best options to meet your organization’s training and development goals.

More information on how to do this is provided in the sections that follow. If you have usage data from an existing application, then ideally the training data for the initial model should be drawn from the usage data for that application. This section provides best practices around selecting training data from usage data. By using a general intent and defining the entities SIZE and MENU_ITEM, the model can learn about these entities across intents, and you don’t need examples containing each entity literal for each relevant intent.

Link your entities to your intents

The data collected from applications can then be brought back in to Mix.nlu via the Discover tab. Detailed information about any errors and warnings encountered during training is provided as a downloadable log file in CSV format. If any errors are encountered, an error log file is generated describing errors and also any warnings. Training is the process of building a model based on the data that you have provided. There is an indicator on the row above the samples indicating how many samples are currently selected out of how many total samples.

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. Samples with invalid characters and entity literals and values with invalid characters are skipped in training but the training will continue. Such a sample is set to excluded in the training set so that it will not be used in the next training run or build. Client applications harness dialog models using the Dialog as a Service gRPC API.

Include anaphora references in samples

For example, you might want to exclude a sample from the model that does not yet fit the business requirements of your app. Samples assigned to UNASSIGNED_SAMPLES, either via .txt or TRSX file upload or manually in the UI, do not have a status icon. These samples contain no annotations and are excluded from the model. To add AND, OR, or NOT tag modifiers to your annotation, first annotate the entities you want to modify. Then select the entities to modify by clicking the first annotation and then clicking the last annotation.

In the second half of the course, you will pursue an original project in natural language understanding with a focus on following best practices in the field. Additional lectures and materials will cover important topics to help expand and improve your original system, including evaluations and metrics, semantic parsing, and grounded language understanding. You can view sample projects from previous learners in the course here. An entity is a language construct for a property, or particular detail, related to the user’s intent. For example, if the user’s intent is to order an espresso drink, entities might include COFFEE_TYPE, FLAVOR, TEMPERATURE, and so on.

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Part of this care is not only being able to adequately meet expectations for customer experience, but to provide a personalized experience. Accenture reports that 91% of consumers say they are more likely to shop with companies that provide offers and recommendations that are relevant to them specifically. Intent recognition identifies what the person speaking or writing intends to do.

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The Results area shows the interpretation of the sentence by the model with the highest confidence. In the image here, the Results area displays the orderCoffee intent with a confidence score of 1.00. The Results area also shows any entity annotations the model has been able to identify. If you have imported one or more prebuilt domains, click the Train Model button to choose to include your own data and/or the prebuilt domains. Since some prebuilt domains are quite large and complex, you may not want to include them when training your model.

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Once the entity has been identified as referable, you can annotate a sample containing an anaphora reference to that entity. For example, within the nuance_DURATION entity, there is a grammar that defines expressions such as “3.5 hours”, “25 mins”, “for 33 minutes and 19 seconds”, and so on. It would simply not make sense to try to capture the possible expressions for this entity in a list. Similarly, you might use entities with regex-based collection to match account numbers, postal (zip) codes, confirmation codes, PINs, or driver’s license numbers, and other pattern-based formats.