Daniel Fried
Valentina Pyatkin Homepage
Nathan Schneider
Implicitness and underspecification are ubiquitous in language. Specifically, language utterances may contain empty or fuzzy elements, such as the following: units of measurement, as in she is 30 vs. it costs 30 (30 what?), bridges and other missing links, as in she tried to enter the car, but the door was stuck (the door of what?), implicit semantic roles, as in I met her while driving (who was driving?) , and various sorts of gradable phenomena; is a small elephant smaller than a big bee ? Even though the
In particular, (implicit) domain restrictions reveal the problem of context dependence of the interpretation of utterances (Stanley and Gendler Szab ́o, 2000). While certain expressions might not be underspecified per se, their interpretation is implicitly restricted by the broader discourse they appear in. This is especially valid for expressions with quantifiers such as every marble is red , which is only true for certain sets of marbles. The problem of underspecified language also extends to pragmatics:
Despite the recent advancements on various semantic tasks, modeling implicitness and underspecification remains a challenging problem in NLP because the elements are not realized on the surface level. Instead of relying on superficial patterns, models are required to leverage contextual aspects of discourse that go beyond the sentence-level. Often, there is a lack of available resources to train such models due to the need for human annotation. Multiple datasets and tasks targeting implicit phenomena have b
Daniel Fried
Valentina Pyatkin Homepage
Nathan Schneider