Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of ${}^{14}C$ in LS, the photons induced by the $\beta$ decay of the ${}^{14}C$ isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag ${}^{14}C$ photon hits in $e^{+}$ events with ${}^{14}C$ pile-up, thereby suppressing its impact on the energy resolution at the hit level: a gated spatiotemporal graph neural network and two Transformer-based models with scalar and vector charge encoding. For a simulated test dataset in which each event contains one ${}^{14}C$ and one $e^{+}$ with kinetic energy below 5~MeV, the models achieve ${}^{14}C$ recall rates of 25%–48% while maintaining $e^{+}$ to ${}^{14}C$ misidentification below 1%, leading to a large improvement in the resolution of total charge for events where $e^{+}$ and ${}^{14}C$ photon hits strongly overlap in space and time.