We thank all reviewers for their detailed and valuable feedback. In the following, we address the reviewers' comments We did not intend to claim we are the first to propose gaze integration in NLP . To the best of our knowledge, no previous works studied gaze integration for the paraphrase generation task. The authors did not reply to our requests for details on the splits. All ablations are inferior to our full model.

Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences. Focusing on a more granular extension of the problem, we propose a Joint Triplet Loss Learning (JTLL) module for the Next New ($N^2$) POI recommendation task, which is more challenging. Our JTLL module first computes additional training samples from the users' historical POI visit sequence, then, a designed triplet loss function is proposed to decrease and increase distances of POI and user embeddings based on their respective relations. Next, the JTLL module is jointly trained with recent approaches to additionally learn unvisited relations for the recommendation task. Experiments conducted on two known real-world LBSN datasets show that our joint training module was able to improve the performances of recent existing works.