  Text classification must sometimes be applied in a low-resource language with
no labeled training data. However, training data may be available in a related
language. We investigate whether character-level knowledge transfer from a
related language helps text classification. We present a cross-lingual document
classification framework (CACO) that exploits cross-lingual subword similarity
by jointly training a character-based embedder and a word-based classifier. The
embedder derives vector representations for input words from their written
forms, and the classifier makes predictions based on the word vectors. We use a
joint character representation for both the source language and the target
language, which allows the embedder to generalize knowledge about source
language words to target language words with similar forms. We propose a
multi-task objective that can further improve the model if additional
cross-lingual or monolingual resources are available. Experiments confirm that
character-level knowledge transfer is more data-efficient than word-level
transfer between related languages.
