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pro vyhledávání: '"Donatelli, Lucia"'
Autor:
Lukin, Stephanie M., Bonial, Claire, Marge, Matthew, Hudson, Taylor, Hayes, Cory J., Pollard, Kimberly A., Baker, Anthony, Foots, Ashley N., Artstein, Ron, Gervits, Felix, Abrams, Mitchell, Henry, Cassidy, Donatelli, Lucia, Leuski, Anton, Hill, Susan G., Traum, David, Voss, Clare R.
Publikováno v:
2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) https://aclanthology.org/2024.lrec-main.1259/
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human pa
Externí odkaz:
http://arxiv.org/abs/2411.12844
Autor:
Bonial, Claire, Lukin, Stephanie M., Abrams, Mitchell, Baker, Anthony, Donatelli, Lucia, Foots, Ashley, Hayes, Cory J., Henry, Cassidy, Hudson, Taylor, Marge, Matthew, Pollard, Kimberly A., Artstein, Ron, Traum, David, Voss, Clare R.
Publikováno v:
Language Resources and Evaluation 2024
In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable comm
Externí odkaz:
http://arxiv.org/abs/2411.12829
Autor:
Mughal, Muhammad Hamza, Dabral, Rishabh, Habibie, Ikhsanul, Donatelli, Lucia, Habermann, Marc, Theobalt, Christian
Gestures play a key role in human communication. Recent methods for co-speech gesture generation, while managing to generate beat-aligned motions, struggle generating gestures that are semantically aligned with the utterance. Compared to beat gesture
Externí odkaz:
http://arxiv.org/abs/2403.17936
We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics. AMR parsers now obtain high scores on the standard AMR evaluation metric Smatch, cl
Externí odkaz:
http://arxiv.org/abs/2312.03480
Autor:
Cao, Yong, Kementchedjhieva, Yova, Cui, Ruixiang, Karamolegkou, Antonia, Zhou, Li, Dare, Megan, Donatelli, Lucia, Hershcovich, Daniel
Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simpl
Externí odkaz:
http://arxiv.org/abs/2310.17353
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical generalization, the interpretation of novel lexical items in syntactic str
Externí odkaz:
http://arxiv.org/abs/2310.15040
Autor:
Wein, Shira, Donatelli, Lucia, Ricker, Ethan, Engstrom, Calvin, Nelson, Alex, Schneider, Nathan
The Abstract Meaning Representation (AMR) formalism, designed originally for English, has been adapted to a number of languages. We build on previous work proposing the annotation of AMR in Spanish, which resulted in the release of 50 Spanish AMR ann
Externí odkaz:
http://arxiv.org/abs/2204.07663
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. We present a systematic comparison of sequence-to-se
Externí odkaz:
http://arxiv.org/abs/2202.11937
The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. These MRs exhibit structural differences that reflect different theoretical and design
Externí odkaz:
http://arxiv.org/abs/2004.14236
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