  Many language generation models are now available for a wide range of
generation tasks, including machine translation and summarization. Combining
such diverse models may lead to further progress, but ensembling generation
models is challenging during inference: conventional ensembling methods (e.g.,
shallow fusion) require that the models share vocabulary/tokenization schemes.
We introduce Twist decoding, a simple and general text generation algorithm
that benefits from diverse models at inference time. Our method does not assume
the vocabulary, tokenization or even generation order is shared. Our extensive
evaluations on machine translation and scientific paper summarization
demonstrate that Twist decoding substantially outperforms each model decoded in
isolation over various scenarios, including cases where domain-specific and
general-purpose models are both available. Twist decoding also consistently
outperforms the popular reranking heuristic where output candidates from one
model are rescored by another. We hope that our work will encourage researchers
and practitioners to examine generation models collectively, not just
independently, and to seek out models with complementary strengths to the
currently available models. Our code is available at
https://github.com/jungokasai/twist_decoding.
