documentation update

README.md

@@ -20,9 +20,7 @@ The toolkit has been built based on our experience from the field and is a great
 - Scoring of STT-transcriptions on an existing LUIS-model.
 
 ### (Optional) Batch-Generation of Textual Training Data
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-Based on our experience in the field, we see that often a larger corpus of training data is needed for STT engines and LUIS models. The correct transcription and recognition of entities such as cities and names often play a significant role. Sometimes there is a lack of training data here, which is why we have created a small tool for duplicating utterances based on different entity types. If this also applies to your use-case, then we recommend taking a look at this [Jupyter Notebook](notebooks/Data - Training Data Generator.ipynb), which will guide you through the necessary steps.
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+Based on our experience in the field, we see that often a larger corpus of training data is needed for STT engines and LUIS models. The correct transcription and recognition of entities such as cities and names often play a significant role. Sometimes there is a lack of training data here, which is why we have created a small tool for duplicating utterances based on different entity types. If this also applies to your use-case, then we recommend taking a look at this [Jupyter Notebook](notebooks/Data\ -\ Training\ Data\ Generator.ipynb), which will guide you through the necessary steps.
 
 ## Getting Started
 This section describes how you get started with GLUE and which requirements need to be fulfilled by your working environment.