This paper studies corpus-based process to select a system-response usable both in chatterbot or as a fallback strategy. It presents, evaluates and compares two selection methods that retrieve and adapt a system-response from the OpenSubtitles2016 corpus given a human-utterance. A corpus of 800 annotated pairs is constituted. Evaluation consists in objective metrics and subjective annotation based on the validity schema proposed in the RE-WOCHAT shared task. Our study indicates that the task of assessing the validity of a system-response given a human-utterance is subjective to an important extent, and is thus a difficult task. Comparisons show that the selection method based on word embedding performs objectively better than the one based on TF-IDF in terms of response variety and response length.