We present a Character-Word Long ShortTerm Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.
REF directly concatenated word and character embeddings without other subnetworks to encode the characters for English and Dutch.
Character-Word LSTM Language Models
European Chapter of the Association for Computational Linguistics (EACL) 2017, Valencia, Spain, pp. 417-427
Computer Science