The rapid progress of Natural Language Processing (NLP) technologies has led to the
widespread availability and effectiveness of text generation tools such as ChatGPT and …

Sequence-to-sequence models have shown strong performance across a broad range of
applications. However, their application to parsing and generating text usingAbstract …

We propose an attention-based model that treats AMR parsing as sequence-to-graph
transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic …

Meaning Representation (AMR) is a semantic representation for natural language that
embeds annotations related to traditional tasks such as named entity recognition, semantic …

We unify different broad-coverage semantic parsing tasks under a transduction paradigm,
and propose an attention-based neural framework that incrementally builds a meaning …

We present the first parser for UCCA, a cross-linguistically applicable framework for
semantic representation, which builds on extensive typological work and supports rapid …

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and
Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch …

With an ever increasing size of text present on the Internet, automatic summary generation
remains an important problem for natural language understanding. In this work we explore a …

We present a transition-based AMR parser that directly generates AMR parses from plain
text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our …

This paper introduces a simple yet effective transition-based system for Abstract Meaning
Representation (AMR) parsing. We argue that a well-defined search space involved in a …