Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a
semantic parsing component to understand which action (s) to execute for an utterance …

This paper proposes the problem of Deep Question Generation (DQG), which aims to
generate complex questions that require reasoning over multiple pieces of information of the …

Abstract The 2020 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and …

The structured representation for semantic parsing in task-oriented assistant systems is
geared towards simple understanding of one-turn queries. Due to the limitations of the …

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

Even though many recent semantic parsers are based on deep learning methods, we
should not forget that rule-based alternatives might offer advantages over neural …

Current open-domain neural semantics parsers show impressive performance. However,
closer inspection of the symbolic meaning representations they produce reveals significant …

Pre-trained language models (PLMs) have achieved great success in NLP and have
recently been used for tasks in computational semantics. However, these tasks do not fully …

Abstract The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with
a focus on semantic parsing and text generation. Currently, we witness an excellent …

In contemporary artificial intelligence, the challenge is making intricate connectionist
systems—comprising millions of parameters—more comprehensible, defensible, and …