Papers by Lucia Donatelli
SPOTTER: A Framework for Investigating Convention Formation in a Visually Grounded Human-Robot Reference Task (2024.lrec-main)
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| Challenge: | Existing research has shown that conventions arise in repeated interactions over the same task, leading to a decrease in utterance length while maintaining informative content. |
| Approach: | They propose to elicit conventions for members of an inner circle of well-known individuals in common ground, as opposed to individuals from an outer circle, who are unfamiliar. |
| Outcome: | The proposed game platform elicits conventions for familiar and unfamiliar individuals in human-robot interaction. |
Normalizing Compositional Structures Across Graphbanks (2020.coling-main)
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| Challenge: | Graph-based meaning representations (MRs) exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. |
| Approach: | They propose a method to normalize MRs at the compositional level by linguistically-grounded rules. |
| Outcome: | The proposed method increases the match in compositional structure between MRs and improves multi-task learning in a low-resource setting. |
Abstract Meaning Representation for Gesture (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation (AMR) is an annotated graphbased representation that expresses the meaning of a sentence in terms of its predicate-argument structure. |
| Approach: | They propose an extension to Abstract Meaning Representation (AMR) that captures the meaning of gesture. |
| Outcome: | The proposed model is more challenging than standard AMR while integrating meaningful elements unique to gesture. |
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (2023.emnlp-main)
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| Challenge: | Abstract Meaning Representation parsers have improved in recent years, but not solved. |
| Approach: | They propose an evaluation suite that evaluates AMR parsers on a range of phenomena . they find that current parser outputs are far from satisfactory . |
| Outcome: | The proposed evaluation suite reveals the abilities and shortcomings of current parsers. |
SLOG: A Structural Generalization Benchmark for Semantic Parsing (2023.emnlp-main)
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| Challenge: | Existing compositional generalization benchmarks focus on lexical generalisation, the interpretation of novel lexicals in syntactic structures familiar from training. |
| Approach: | They propose a semantic parsing dataset that extends COGS with 17 structural generalization cases to evaluate how well models generalize to new complex linguistic expressions. |
| Outcome: | The proposed model generalization accuracy is far below the near-perfect accuracy of existing models on COGS, demonstrating the role of SLOG in foregrounding the large discrepancy between models’ lexical and structural generalization capacities. |
Aligning Actions Across Recipe Graphs (2021.emnlp-main)
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| Challenge: | a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order. |
| Approach: | They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding . |
| Outcome: | The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all . |
Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) was designed to represent sentence meaning in English text, but recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. |
| Approach: | They propose to annotate a multimodal (speech and gesture) AMR corpus in a task-based setting and capture coreference relationships across modalities. |
| Outcome: | The proposed corpus captures coreference relationships across modalities, enabling fine-grained analysis of how gesture and natural language interact. |
A Two-Level Interpretation of Modality in Human-Robot Dialogue (2020.coling-main)
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| Challenge: | modal expressions are used to communicate and align world knowledge, but there is no obvious manner to ground them in the shared environment. |
| Approach: | They propose a two-level annotation scheme for modality that captures both content and intent and a task-oriented, pragmatic representation that maps to our robot's capabilities. |
| Outcome: | The proposed model can be grounded and dynamically interpreted. |
Compositional generalization with a broad-coverage semantic parser (2022.starsem-1)
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| Challenge: | Recent work has shown that compositional generalization on COGS is difficult and complex. |
| Approach: | They propose a compositional semantic parser that solves compositional generalization on COGS dataset. |
| Outcome: | The AM parser solves compositional generalization on the COGS dataset. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |
More frequent verbs are associated with more diverse valency frames: Efficient principles at the lexicon-grammar interface (2024.acl-long)
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| Challenge: | Existing evidence has focused on word-internal properties, such as Zipf's observation that more frequent words are optimized in form to minimize communicative cost. |
| Approach: | They propose to examine the hypothesis that efficient lexicon organization is also reflected in valency, or the combinations and orders of additional words and phrases a verb selects for in a sentence. |
| Outcome: | The proposed hypothesis is consistent with communicative efficiency principles. |
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)
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Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor A. Hudson, Cory J. Hayes, Kimberly Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare Voss
| Challenge: | The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
| Approach: | They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. |
| Outcome: | The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |