Brendan T. O'Connor - UMass Amherst, Computer Science
Brendan T. O'Connor - UMass Amherst, Computer Science
Recent developments in NLP have led to excellent performance on various semantic tasks. However, an important question that remains open is whether such methods are actually capable of modeling how linguistic meaning is shaped and influenced by context, or if they simply learn superficial patterns that reflect only explicitly stated aspects of meaning. An interesting case in point is the interpretation and understanding of implicit or underspecified language.
More concretely, 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"? Where is the boundary between "orange" and "red"?
Implicit and underspecified phenomena have been studied in linguistics and philosophy for decades (Sag, 1976; Heim, 1982; Ballmer and Pinkal, 1983), but empirical studies in NLP are scarce and far between. The number of datasets and task proposals is however growing (Roesiger et al., 2018; Elazar and Goldberg, 2019; Ebner et al., 2020; McMahan and Stone, 2020) and recent studies have shown the difficulty of annotating and modeling implicit and underspecified phenomena (Shwartz and Dagan, 2016; Scholman and
Brendan T. O'Connor - UMass Amherst, Computer Science
Brendan T. O'Connor - UMass Amherst, Computer Science