  Generative commonsense question answering (GenCQA) is a task of automatically
generating a list of answers given a question. The answer list is required to
cover all reasonable answers. This presents the considerable challenges of
producing diverse answers and ranking them properly. Incorporating a variety of
closely-related background knowledge into the encoding of questions enables the
generation of different answers. Meanwhile, learning to distinguish positive
answers from negative ones potentially enhances the probabilistic estimation of
plausibility, and accordingly, the plausibility-based ranking. Therefore, we
propose a Knowledge Enhancement and Plausibility Ranking (KEPR) approach
grounded on the Generate-Then-Rank pipeline architecture. Specifically, we
expand questions in terms of Wiktionary commonsense knowledge of keywords, and
reformulate them with normalized patterns. Dense passage retrieval is utilized
for capturing relevant knowledge, and different PLM-based (BART, GPT2 and T5)
networks are used for generating answers. On the other hand, we develop an
ELECTRA-based answer ranking model, where logistic regression is conducted
during training, with the aim of approximating different levels of plausibility
in a polar classification scenario. Extensive experiments on the benchmark
ProtoQA show that KEPR obtains substantial improvements, compared to the strong
baselines. Within the experimental models, the T5-based GenCQA with KEPR
obtains the best performance, which is up to 60.91% at the primary canonical
metric Inc@3. It outperforms the existing GenCQA models on the current
leaderboard of ProtoQA.
