We use character bi-LSTMs to handle the Out Of Vocabulary (OOV) problem as in (Ling et al., 2015b).
just as a distributional hypothesis exists for words, prior work (Tsvetkov and Dyer, 2015;Tsvetkov et al., 2015) suggests phonological character representations capture inherent similarities between characters that are not apparent from orthogonal one-hot orthographic character representations and can serve as a language universal surrogate for character representations.