no code implementations • LREC 2022 • Richard Brutti, Lucia Donatelli, Kenneth Lai, James Pustejovsky
This paper presents Gesture AMR, an extension to Abstract Meaning Representation (AMR), that captures the meaning of gesture.
1 code implementation • DMR (COLING) 2020 • Kenneth Lai, Lucia Donatelli, James Pustejovsky
Abstract Meaning Representation (AMR) is a simple, expressive semantic framework whose emphasis on predicate-argument structure is effective for many tasks.
1 code implementation • *SEM (NAACL) 2022 • Pia Weißenhorn, Lucia Donatelli, Alexander Koller
We show how the AM parser, a compositional semantic parser (Groschwitz et al., 2018) can solve compositional generalization on the COGS dataset.
1 code implementation • EMNLP 2021 • Lucia Donatelli, Theresa Schmidt, Debanjali Biswas, Arne Köhn, Fangzhou Zhai, Alexander Koller
Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding.
no code implementations • EMNLP (LAW, DMR) 2021 • Katharina Stein, Lucia Donatelli
Abstract Meaning Representation (AMR) has become popular for representing the meaning of natural language in graph structures.
no code implementations • 26 Mar 2024 • Muhammad Hamza Mughal, Rishabh Dabral, Ikhsanul Habibie, Lucia Donatelli, Marc Habermann, Christian Theobalt
Gestures play a key role in human communication.
1 code implementation • 6 Dec 2023 • Jonas Groschwitz, Shay B. Cohen, Lucia Donatelli, Meaghan Fowlie
We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics.
no code implementations • 26 Oct 2023 • Yong Cao, Yova Kementchedjhieva, Ruixiang Cui, Antonia Karamolegkou, Li Zhou, Megan Dare, Lucia Donatelli, Daniel Hershcovich
We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines.
1 code implementation • 23 Oct 2023 • Bingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen, Yuekun Yao, Najoung Kim
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions.
no code implementations • 15 Apr 2022 • Shira Wein, Lucia Donatelli, Ethan Ricker, Calvin Engstrom, Alex Nelson, Nathan Schneider
The Abstract Meaning Representation (AMR) formalism, designed originally for English, has been adapted to a number of languages.
no code implementations • 24 Feb 2022 • Pia Weißenhorn, Yuekun Yao, Lucia Donatelli, Alexander Koller
A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into unseen sequences.
no code implementations • COLING 2020 • Lucia Donatelli, Kenneth Lai, James Pustejovsky
We analyze the use and interpretation of modal expressions in a corpus of situated human-robot dialogue and ask how to effectively represent these expressions for automatic learning.
no code implementations • LREC 2020 • Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron artstein, David Traum, Clare Voss
This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.
1 code implementation • COLING 2020 • Lucia Donatelli, Jonas Groschwitz, Alexander Koller, Matthias Lindemann, Pia Weißenhorn
The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure.
no code implementations • CONLL 2019 • Lucia Donatelli, Meaghan Fowlie, Jonas Groschwitz, Alex Koller, er, Matthias Lindemann, Mario Mina, Pia Wei{\ss}enhorn
We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).
no code implementations • WS 2019 • Claire Bonial, Lucia Donatelli, Stephanie M. Lukin, Stephen Tratz, Ron artstein, David Traum, Clare Voss
We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance.
no code implementations • COLING 2018 • Jamal Laoudi, Claire Bonial, Lucia Donatelli, Stephen Tratz, Clare Voss
In this paper, we explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide but which only recently has begun appearing frequently in written form in social media.
no code implementations • COLING 2018 • Lucia Donatelli, Michael Regan, William Croft, Nathan Schneider
Although English grammar encodes a number of semantic contrasts with tense and aspect marking, these semantics are currently ignored by Abstract Meaning Representation (AMR) annotations.