Update README.md

README.md

@@ -195,7 +195,7 @@ task_categories:
 - question-answering
 language:
 - en
-pretty_name: QuAnT
+pretty_name: QuAnTS
 size_categories:
 - 100K<n<1M
 ---
@@ -204,7 +204,7 @@ size_categories:
 
 QuAnTS is a challenging dataset designed to bridge the gap in question-answering research on time series data.
 The dataset features a wide variety of questions and answers concerning human movements, presented as tracked skeleton trajectories.
-To benchmark the practical usability of models trained on this dataset, QuAnTS also includes human reference performance.
+QuAnTS also includes human reference performance to benchmark the practical usability of models trained on this dataset.
 
 ![Q: "What is the person doing first?", A: "They are waving.", Q: "How many times are they jumping after that?", A: "..."](doc/intro-chat.png "Example chat motivating time series question answering")
 
@@ -216,15 +216,15 @@ At present, there is no official leaderboard for this dataset.
 
 The primary task for the QuAnTS dataset is Time Series Question Answering.
 Given a time series of human skeleton trajectories and a question in natural language, the goal is to generate a correct answer.
-Answers are provided in one of the following formats binary (Yes/No), multiple-choice (A/B/C), or open (free text).
-Additionally, to provide more training data for free text answers, we provide fully textual answers for binary and multiple-choice, too. 
-The ground truth action sequence or scene descriptions *may not* be used for answering the dataset — they are provided for debugging purposes.
+Answers are provided in one of the following formats: binary (Yes/No), multiple-choice (A/B/C), or open (free text).
+Additionally, to provide more training data for free-text answers, we provide entirely textual answers for all binary and multiple-choice questions. 
+The ground truth action sequence or scene descriptions *may not* be used to answer the dataset — we provide them for debugging purposes only.
 The text in the dataset is in English.
 
 We provide fixed splits into training, validation, and test portions, where only the latter may be used to compare performance across different approaches.
 You are free to mix the training and validation splits as needed.
 
-## Licensing, Citation, and Acknowlegements
+## Licensing, Citation, and Acknowledgments
 
 The QuAnTS dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](ttps://creativecommons.org/licenses/by/4.0/ ) license.