Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

We combine character-level and contextual language model representations to improve
performance on Discourse Representation Structure parsing. Character representations can …

Semantic parsing offers many opportunities to improve natural language understanding. We
present a semantically annotated parallel corpus for English, German, Italian, and Dutch …

The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to
produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate …

Semantics and pragmatics-the study of meaning, and meaning in context, respectively-are
two fundamental areas of linguistics, and as such are crucial to our understanding of how …

We consider the task of crosslingual semantic parsing in the style of Discourse
Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich …

Discourse understanding is hampered when missing or conflicting context information is
given. In four experiments, we investigated what happens (a) when the definite determiner …

We propose neural models to generate text from formal meaning representations based on
Discourse Representation Structures (DRSs). DRSs are document-level representations …

If we want to develop open-domain, robust semantic parsers for Discourse Representation
Theory, a formalism that received considerable attention in formal semantics, then we need …

The AMR (Abstract Meaning Representation) formalism for representing meaning of natural
language sentences was not designed to deal with scope and quantifiers. By extending …