  We propose a novel neural sequence prediction method based on
\textit{error-correcting output codes} that avoids exact softmax normalization
and allows for a tradeoff between speed and performance. Instead of minimizing
measures between the predicted probability distribution and true distribution,
we use error-correcting codes to represent both predictions and outputs.
Secondly, we propose multiple ways to improve accuracy and convergence rates by
maximizing the separability between codes that correspond to classes
proportional to word embedding similarities. Lastly, we introduce our main
contribution called \textit{Latent Variable Mixture Sampling}, a technique that
is used to mitigate exposure bias, which can be integrated into training latent
variable-based neural sequence predictors such as ECOC. This involves mixing
the latent codes of past predictions and past targets in one of two ways: (1)
according to a predefined sampling schedule or (2) a differentiable sampling
procedure whereby the mixing probability is learned throughout training by
replacing the greedy argmax operation with a smooth approximation. ECOC-NSP
leads to consistent improvements on language modelling datasets and the
proposed Latent Variable mixture sampling methods are found to perform well for
text generation tasks such as image captioning.
