  The Backpack is a Transformer alternative shown to improve interpretability
in English language modeling by decomposing predictions into a weighted sum of
token sense components. However, Backpacks' reliance on token-defined meaning
raises questions as to their potential for languages other than English, a
language for which subword tokenization provides a reasonable approximation for
lexical items. In this work, we train, evaluate, interpret, and control
Backpack language models in character-tokenized Chinese, in which words are
often composed of many characters. We find that our (134M parameter) Chinese
Backpack language model performs comparably to a (104M parameter) Transformer,
and learns rich character-level meanings that log-additively compose to form
word meanings. In SimLex-style lexical semantic evaluations, simple averages of
Backpack character senses outperform input embeddings from a Transformer. We
find that complex multi-character meanings are often formed by using the same
per-character sense weights consistently across context. Exploring
interpretability-through control, we show that we can localize a source of
gender bias in our Backpacks to specific character senses and intervene to
reduce the bias.
