In　E-Commence　applications,　online　shoppers　increasingly　rely　on　reviews　to　find　answers　for　their　queries　on　products.　Recently,　there　has　been　significant　research　on　product　question　answering　using　reviews.　While　many　neural　models　have　been　proposed　to　generate　answers　from　reviews　to　answer　product　questions,　the　evaluation　of　review-based　product　question　answering　(RPQA)　models　is　still　largely　using　lexicon-based　text　similarity　metrics　against　reference　answers.　In　this　paper,　we　propose　a　distant　supervised　contrastive　learning　network　based　on　modern　transformer-based　language　models　by　leveraging　distant　ground-truth　community　question　answers　to　train　a　model　for　RPQA　evaluation.　At　deployment,　the　learned　metric　does　not　require　reference　answers　to　evaluate　the　generated　answers　for　product　queries.　We　conducted　extensive　experiments　on　publicly　available　AmazonQA　datasets　to　evaluate　RPQA　models.　Experiment　results　show　that　our　learned　metric　correlates　well　with　human　judgements,　and　outperforms　existing　lexicon-based　or　embedding-based　metrics.　©　2024　IEEE.