  End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into
the so-called "likelihood trap", resulting in generated responses which are
dull, repetitive, and often inconsistent with dialogue history. Comparing
ranked lists of multiple generated responses against the "gold response" (from
evaluation data) reveals a wide diversity in response quality, with many good
responses placed lower in the ranked list. The main challenge, addressed in
this work, is then how to reach beyond greedily generated system responses,
that is, how to obtain and select such high-quality responses from the list of
overgenerated responses at inference without availability of the gold response.
To this end, we propose a simple yet effective reranking method which aims to
select high-quality items from the lists of responses initially overgenerated
by the system. The idea is to use any sequence-level (similarity) scoring
function to divide the semantic space of responses into high-scoring versus
low-scoring partitions. At training, the high-scoring partition comprises all
generated responses whose similarity to the gold response is higher than the
similarity of the greedy response to the gold response. At inference, the aim
is to estimate the probability that each overgenerated response belongs to the
high-scoring partition, given only previous dialogue history. We validate the
robustness and versatility of our proposed method on the standard MultiWOZ
dataset: our methods improve a state-of-the-art E2E ToD system by 2.0 BLEU, 1.6
ROUGE, and 1.3 METEOR scores, achieving new peak results. Additional
experiments on the BiTOD dataset and human evaluation further ascertain the
generalisability and effectiveness of the proposed framework.
