This paper presents a systematic approach to using the Socratic method in develo**
prompt templates that effectively interact with large language models, including GPT-3 …

Existing studies usually extract the sentiment elements by decomposing the complex
structure prediction task into multiple subtasks. Despite their effectiveness, these methods …

Large language models (LLMs) show amazing proficiency and fluency in the use of
language. Does this mean that they have also acquired insightful linguistic knowledge about …

We propose KnowGL, a tool that allows converting text into structured relational data
represented as a set of ABox assertions compliant with the TBox of a given Knowledge …

AMR parsing has experienced an unprecendented increase in performance in the last three
years, due to a mixture of effects including architecture improvements and transfer learning …

Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word
alignments. These alignments are learned separately from parser training and require a …

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 …

Voice assistants interrupt people when they pause mid-question, a frustrating interaction that
requires the full repetition of the entire question again. This impacts all users, but particularly …

Meaning Representation (AMR) parsing aims to translate sentences to semantic
representation with a hierarchical structure, and is recently empowered by pretrained …

Instruction fine-tuned language models on a collection of instruction annotated datasets
(FLAN) have shown highly effective to improve model performance and generalization to …