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 Demberg, 2017; Webber et al., 2019).

The use of implicit and underspecified terms poses serious challenges to standard natural language processing models, and they often require incorporating greater context, using symbolic inference and common-sense reasoning, or more generally, going beyond strictly lexical and compositional meaning constructs. This challenge spans all phases of the NLP model's life cycle: from collecting and annotating relevant data, through devising computational methods for modelling such phenomena, to evaluating and designing proper evaluation metrics.

Furthermore, most existing efforts in NLP are concerned with one particular problem, their benchmarks are narrow in size and scope, and no common platform or standards exist for studying effects on downstream tasks. In our opinion, interpreting implicit and underspecified language is an inherent part of natural language understanding, these elements are essential for human-like interpretation, and modeling them may be critical for downstream applications.

The goal of this workshop is to bring together theoreticians and practitioners from the entire NLP cycle, from annotation and benchmarking to modeling and applications, and to provide an umbrella for the development, discussion and standardization of the study of understanding implicit and underspecified language. We solicit papers on the following, and other, topics:

• Verb-phrase ellipsis and syntactic gaps
• Implicit semantic roles and semantic relations
• Bridging anaphora
• Gradable/imprecise terms
• Fused heads
• Other phenomena that involve underspecification or implicit information

As part of the workshop, we are organizing a shared task on implicit and underspecified language. The focus of this task is on modeling the necessity of clarifications due to aspects of meaning that are implicit or underspecified in context. Specifically, the task setting follows the recent proposal of predicting revision requirements in collaboratively edited instructions (Bhat et al., 2020). The data consists of instances from wikiHowToImprove (Anthonio et al., 2020) in which a revision resolved an implicit or underspecified linguist element. The following revision types are part of the data:

• Replacements of pronouns with more precise noun phrases
• Replacements of 'do' as a full verb with more precise verbs
• Insertions of optional verbal phrase complements
• Insertions of adverbial and adjectival modifiers
• Insertions of logical quantifiers and modal verbs

Final training and development sets are available here:

• Training data (unverified)
• Development data (manually verified)
• README (updated 5 March 2021)

Access to the test data requires registration as a participant. If you are interested in participating in the shared task, please contact Michael Roth.

• December 21, 2020: First call for workshop papers
• February 15, 2021: Second call for workshop papers
• April 14, 2021: Start of shared task evaluation
• April 30, 2021: Regular workshop paper due date
• May 3, 2021: End of shared task evaluation
• May 21, 2021: Shared task papers due date
• May 28, 2021: Notification of acceptance
• June 7, 2021: Camera-ready papers due
• August 5, 2021: Workshop date

• Michael Roth, Stuttgart University
• Reut Tsarfaty, Bar-Ilan University
• Yoav Goldberg, Bar-Ilan University and AI2

Program Committee

• Omri Abend, Hebrew University of Jerusalem
• Johan Bos, University of Groningen
• Nancy Chang, Google
• Vera Demberg, Saarland University
• Katrin Erk, University of Texas at Austin
• Annemarie Friedrich, Bosch Center for Artificial Intelligence
• Dan Goldwasser, Purdue University
• Yufang Hou, IBM Research Ireland
• Ruihong Huang, Texas A&M University
• Mirella Lapata, University of Edinburgh
• Junyi Jessy Li, University of Texas at Austin
• Ray Mooney, University of Texas at Austin
• Philippe Muller, University of Toulouse
• Vincent Ng, University of Texas at Dallas
• Tim O'Gorman, University of Massachusetts Amherst
• Karl Pichotta, Memorial Sloan Kettering Cancer Center
• Massimo Poesio, Queen Mary University
• Niko Schenk, Amazon
• Nathan Schneider, Georgetown University
• Vered Shwartz, Allen Institute for AI & University of Washington
• Elior Sulem, University of Pennsylvania
• Sara Tonelli, Fondazione Bruno Kessler
• Ben Van Durme, Johns Hopkins University & Microsoft Semantic Machines
• Luke Zettlemoyer, University of Washington & Facebook