2 citations · 6 across the 5 of their papers we have counts for
7 papers
Zero and Few-shot Semantic Parsing with Ambiguous Inputs
Elias Stengel-Eskin, Kyle Rawlins, Benjamin Van Durme
Despite the frequent challenges posed by ambiguity when representing meaning via natural language, it is often ignored or deliberately removed in tasks mapping language to formally…
LOME: Large Ontology Multilingual Extraction
Patrick Xia, Guanghui Qin, Siddharth Vashishtha +7
We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions…
Multi-Sentence Argument Linking
Seth Ebner, Patrick Xia, Ryan Culkin +2
We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference res…
The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha +9
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantic…
Predicting the Argumenthood of English Prepositional Phrases
Najoung Kim, Kyle Rawlins, Benjamin Van Durme +1
Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such a…
Lexicosyntactic Inference in Neural Models
Aaron Steven White, Rachel Rudinger, Kyle Rawlins +1
We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event…