We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.

Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation – along with MWE-annotated training sets and corpus-based lists of MWEs – for reproduction and extension.

The theoretical characterisation of multiword expressions (MWEs) is tightly connected to their actual occurrences in data and to their representation in lexical resources. We present three lexical resources for Italian MWEs, namely an electronic lexicon, a series of example corpora and a database of MWEs represented around morphosyntactic patterns. These resources are matched against, and created from, a very large web-derived corpus for Italian that spans across registers and domains. We can thus test expressions coded by lexicographers in a dictionary, thereby discarding unattested expressions, revisiting lexicographers's choices on the basis of frequency information, and at the same time creating an example sub-corpus for each entry. We organise MWEs on the basis of the morphosyntactic information obtained from the data in an electronic, flexible knowledge-base containing structured annotation exploitable for multiple purposes. We also suggest further work directions towards characterising MWEs by analysing the data organised in our database through lexico-semantic information available in WordNet or MultiWordNet-like resources, also in the perspective of expanding their set through the extraction of other similar compact expressions.