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Recurrent neural networks (RNNs) have reached striking performance in manynatural language processing tasks. This has renewed interest in whether thesegeneric sequence processing devices are inducing genuine linguistic knowledge.Nearly all current analytical studies, however, initialize the RNNs with avocabulary of known words, and feed them tokenized input during training. Wepresent a multi-lingual study of the linguistic knowledge encoded in RNNstrained as character-level language models, on input data with word boundariesremoved. These networks face a tougher and more cognitively realistic task,having to discover any useful linguistic unit from scratch based on inputstatistics. The results show that our "near tabula rasa" RNNs are mostly ableto solve morphological, syntactic and semantic tasks that intuitivelypresuppose word-level knowledge, and indeed they learned, to some extent, totrack word boundaries. Our study opens the door to speculations about thenecessity of an explicit, rigid word lexicon in language learning and usage.

Tabula nearly rasa: Probing the Linguistic Knowledge of Character-Level Neural Language Models Trained on Unsegmented Text