  Contextualized or discourse aware commonsense inference is the task of
generating coherent commonsense assertions (i.e., facts) from a given story,
and a particular sentence from that story. Some problems with the task are:
lack of controllability for topics of the inferred facts; lack of commonsense
knowledge during training; and, possibly, hallucinated or false facts. In this
work, we utilize a transformer model for this task and develop techniques to
address the aforementioned problems in the task. We control the inference by
introducing a new technique we call "hinting". Hinting is a kind of language
model prompting, that utilizes both hard prompts (specific words) and soft
prompts (virtual learnable templates). This serves as a control signal to
advise the language model "what to talk about". Next, we establish a
methodology for performing joint inference with multiple commonsense knowledge
bases. Joint inference of commonsense requires care, because it is imprecise
and the level of generality is more flexible. You want to be sure that the
results "still make sense" for the context. To this end, we align the textual
version of assertions from three knowledge graphs (ConceptNet, ATOMIC2020, and
GLUCOSE) with a story and a target sentence. This combination allows us to
train a single model to perform joint inference with multiple knowledge graphs.
We show experimental results for the three knowledge graphs on joint inference.
Our final contribution is exploring a GAN architecture that generates the
contextualized commonsense assertions and scores them as to their plausibility
through a discriminator. The result is an integrated system for contextual
commonsense inference in stories, that can controllably generate plausible
commonsense assertions, and takes advantage of joint inference between multiple
commonsense knowledge bases.
