Ambiguous requirements are problematic in requirement engineering as various stakeholders can debate on the interpretation of the requirements leading to a variety of issues in the development stages. Since requirement specifications are usually written in natural language, analyzing ambiguous requirements is currently a manual process as it has not been fully automated to meet the industry standards. In this paper, we used transfer learning by using ULMFiT where we pre-trained our model to a general-domain corpus and then fine-tuned it to classify ambiguous vs unambiguous requirements (target task). We then compared its accuracy with machine learning classifiers like SVM, Linear Regression, and Multinomial Naive Bayes. We also used back translation (BT) as a text augmentation technique to see if it improved the classification accuracy. Our results showed that ULMFiT achieved higher accuracy than SVM (Support Vector Machines), Logistic Regression and Multinomial Naive Bayes for our initial data set. Further by augmenting requirements using BT, ULMFiT got a higher accuracy than SVM, Logistic Regression, and Multinomial Naive Bayes classifier, improving the initial performance by 5.371%. Our proposed research provides some promising insights on how transfer learning and text augmentation can be applied to small data sets in requirements engineering.