acrolinx pass

Author: jboback

articles/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances.md

@@ -14,9 +14,9 @@ ms.custom: language-service-clu
 
 # Label your utterances in Language Studio
 
-Once you have [built a schema](build-schema.md) for your project, you should add training utterances to your project. The utterances should be similar to what your users will use when interacting with the project. When you add an utterance, you have to assign which intent it belongs to. After the utterance is added, label the words within your utterance that you want to extract as entities. 
+Once you have [built a schema](build-schema.md) for your project, you should add training utterances to your project. The utterances should be similar to what your users use when interacting with the project. When you add an utterance, you have to assign which intent it belongs to. After the utterance is added, label the words within your utterance that you want to extract as entities. 
 
-Data labeling is a crucial step in development lifecycle; this data will be used in the next step when training your model so that your model can learn from the labeled data. If you already have labeled utterances, you can directly [import it into your project](create-project.md#import-project), but you need to make sure that your data follows the [accepted data format](../concepts/data-formats.md). See [create project](create-project.md#import-project) to learn more about importing labeled data into your project. Labeled data informs the model how to interpret text, and is used for training and evaluation.
+Data labeling is a crucial step in development lifecycle; this data are used in the next step when training your model so that your model can learn from the labeled data. If you already have labeled utterances, you can directly [import it into your project](create-project.md#import-project), but you need to make sure that your data follows the [accepted data format](../concepts/data-formats.md). See [create project](create-project.md#import-project) to learn more about importing labeled data into your project. Labeled data informs the model how to interpret text, and is used for training and evaluation.
 
 ## Prerequisites
 
@@ -28,7 +28,7 @@ See the [project development lifecycle](../overview.md#project-development-lifec
 
 ## Data labeling guidelines
 
-After [building your schema](build-schema.md) and [creating your project](create-project.md), you will need to label your data. Labeling your data is important so your model knows which words and sentences will be associated with the intents and entities in your project. You will want to spend time labeling your utterances - introducing and refining the data that will be used to in training your models.
+After [building your schema](build-schema.md) and [creating your project](create-project.md), you need to label your data. Labeling your data is important so your model knows which words and sentences are associated with the intents and entities in your project. Spend time labeling your utterances - introducing and refining the data that is used in training your models.
 
 As you add utterances and label them, keep in mind:
 
@@ -71,7 +71,7 @@ Use the following steps to label your utterances:
     |Option |Description  |
     |---------|---------|
     |Label using a brush     | Select the brush icon next to an entity in the right pane, then highlight the text in the utterance you want to label.           |
-    |Label using inline menu     | Highlight the word you want to label as an entity, and a menu will appear. Select the entity you want to label these words with. |
+    |Label using inline menu     | Highlight the word you want to label as an entity, and a menu appears. Select the entity you want to label these words with. |
 
 6. In the right side pane, under the **Labels** pivot, you can find all the entity types in your project and the count of labeled instances per each.
 
@@ -105,11 +105,11 @@ Before you get started, the suggest utterances feature is only available if your
 
 In the Data Labeling page: 
 
-1. Select the **Suggest utterances** button. A pane will open up on the right side prompting you to select your Azure OpenAI resource and deployment. 
+1. Select the **Suggest utterances** button. A pane opens up on the right side prompting you to select your Azure OpenAI resource and deployment. 
 2. On selection of an Azure OpenAI resource, select **Connect**, which allows your Language resource to have direct access to your Azure OpenAI resource. It assigns your Language resource the role of `Cognitive Services User` to your Azure OpenAI resource, which allows your current Language resource to have access to Azure OpenAI's service. If the connection fails, follow these [steps](#add-required-configurations-to-azure-openai-resource) below to add the right role to your Azure OpenAI resource manually. 
 3. Once the resource is connected, select the deployment. The recommended model for the Azure OpenAI deployment is `gpt-35-turbo-instruct`.
 4. Select the intent you'd like to get suggestions for. Make sure the intent you have selected has at least 5 saved utterances to be enabled for utterance suggestions. The suggestions provided by Azure OpenAI are based on the **most recent utterances** you've added for that intent. 
-5. Select **Generate utterances**. Once complete, the suggested utterances will show up with a dotted line around it, with the note *Generated by AI*. Those suggestions need to be accepted or rejected. Accepting a suggestion simply adds it to your project, as if you had added it yourself. Rejecting it deletes the suggestion entirely. Only accepted utterances will be part of your project and used for training or testing. You can accept or reject by clicking on the green check or red cancel buttons beside each utterance. You can also use the `Accept all` and `Reject all` buttons in the toolbar. 
+5. Select **Generate utterances**. Once complete, the suggested utterances  show up with a dotted line around it, with the note *Generated by AI*. Those suggestions need to be accepted or rejected. Accepting a suggestion simply adds it to your project, as if you had added it yourself. Rejecting it deletes the suggestion entirely. Only accepted utterances are part of your project and used for training or testing. You can accept or reject by clicking on the green check or red cancel buttons beside each utterance. You can also use the `Accept all` and `Reject all` buttons in the toolbar. 
 
 :::image type="content" source="../media/suggest-utterances.png" alt-text="A screenshot showing utterance suggestions in Language Studio." lightbox="../media/suggest-utterances.png":::
 
@@ -153,7 +153,7 @@ After enabling managed identity, assign the role `Cognitive Services User` to yo
 
 :::image type="content" source="../media/add-role-azure-openai.gif" alt-text="Multiple screenshots showing the steps to add the required role to your Azure OpenAI resource." lightbox="../media/add-role-azure-openai.gif":::
 
-After a few minutes, refresh the Language Studio and you will be able to successfully connect to Azure OpenAI.
+After a few minutes, refresh the Language Studio and you are able to successfully connect to Azure OpenAI.
 
 ## Next Steps
 * [Train Model](./train-model.md)