  Language models built using semi-supervised machine learning on large corpora
of natural language have very quickly enveloped the fields of natural language
generation and understanding. In this paper we apply a zero-shot approach
independently developed by a number of researchers now gaining recognition as a
significant alternative to fine-tuning for evaluation on common sense tasks. A
language model with relatively few parameters and training steps compared to a
more recent language model (T5) can outperform it on a recent large data set
(TimeDial), while displaying robustness in its performance across a similar
class of language tasks. Surprisingly, this result is achieved by using a
hyperparameter-free zero-shot method with the smaller model, compared to
fine-tuning to the larger model. We argue that robustness of the smaller model
ought to be understood in terms of compositionality, in a sense that we draw
from recent literature on a class of similar models. We identify a practical
cost for our method and model: high GPU-time for natural language evaluation.
The zero-shot measurement technique that produces remarkable stability, both
for ALBERT and other BERT variants, is an application of pseudo-log-likelihoods
to masked language models for the relative measurement of probability for
substitution alternatives in forced choice language tasks such as the Winograd
Schema Challenge, Winogrande, and others. One contribution of this paper is to
bring together a number of similar, but independent strands of research. We
produce some absolute state-of-the-art results for common sense reasoning in
binary choice tasks, performing better than any published result in the
literature, including fine-tuned efforts. We show a remarkable consistency of
the model's performance under adversarial settings, which we argue is best
explained by the model's compositionality of representations.
