Papers by Francesco Cazzaro

5 papers
Analyzing Text Representations by Measuring Task Alignment (2023.acl-short)

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Challenge: Recent advances in text classification have shown that pre-trained representations are key for text classification.
Approach: They propose a task alignment score that measures alignment at different levels of granularity.
Outcome: The proposed score shows that task alignment can explain the performance of a given representation.
SPOT: Zero-Shot Semantic Parsing Over Property Graphs (2025.findings-acl)

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Challenge: Knowledge Graphs (KGs) are becoming increasingly popular as a means of storing structured data.
Approach: They propose a method to generate training data for semantic parsing over Property Graphs without human annotations by matching tree patterns to the KG and paraphrasing the query program with an LLM.
Outcome: The proposed method generates training data for parsing over Property Graphs without human annotations on two property graph benchmarks utilizing the Cypher query language.
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)

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Challenge: Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches.
Approach: They propose a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output.
Outcome: The proposed approach improves compositional generalization over existing models and other approaches that exploit gold alignment annotations.
Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)

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Challenge: Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging.
Approach: They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization.
Outcome: The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations.
ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs (2025.findings-emnlp)

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Challenge: PGs are increasingly used in knowledge graphs, but they are underrepresented in research . a benchmark is designed specifically for PG and queries written in Cypher.
Approach: They propose a benchmark specifically for PGs and queries written in Cypher.
Outcome: The proposed benchmark is designed specifically for PGs and queries written in Cypher.

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