Specific case of a ranking model
Build a boolean classifier that classifies a message as either "task" or "not a task".  Choose a classifier that can output a confidence score (many classifiers can, including ensembles like random forests; neural networks and deep learning; logistic regression; and more).

Train the classifier on many messages.

For a collection of 3 messages, apply the classifier to each message and get the confidence score.  Then, use the confidence scores to rank the 3 messages: e.g., choose the message that has the highest confidence/probability of being "task" as the one that is most likely to be the task.

This is different from what you had in mind: here each instance is a message, not a conversation.

Which features to use is a separate question, but you'll probably want to use standard NLP features (bag of words, word2vec, and so on).  That belongs in a separate question.  As a warning, if each message is very short as indicated in your example, the performance of your classifier might not be great.

Also, you will need a large collection of messages where you know the label (classification) of each.