Papers by Simone Ponzetto

8 papers
ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications (2024.naacl-long)

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Challenge: Existing statistical phrasal or hierarchical machine translation systems relies on a large set of translation rules which results in engineering challenges.
Approach: They propose to use factorized grammar from the field of linguistics as more general translation rules from XTAG English Grammar to generate a manually crafted summarization dataset.
Outcome: The proposed method outperforms existing methods on low-resource language translation tasks with less training data.
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)

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Challenge: Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas.
Approach: They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models .
Outcome: The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks .
Fair and Argumentative Language Modeling for Computational Argumentation (2022.acl-long)

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Challenge: Recent work on stereotypical biases in semantic spaces is still in its infancy . we present a novel resource for bias measurement specifically tailored to argumentation .
Approach: They propose a resource for bias measurement specifically tailored to argumentation . they use argumentative fine-tuning and debiasing to assess intrinsic bias .
Outcome: The proposed approach is more sustainable and parameter-efficient than full fine-tuning . it can remove bias in general and argumentative language models while improving model performance in downstream tasks.
GenGO: ACL Paper Explorer with Semantic Features (2024.acl-demos)

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Challenge: Scholarly document processing (SDP) is a powerful tool for researchers to process knowledge stored in research papers.
Approach: They propose a system that allows researchers to search papers published in ACL conferences with metadata and text embeddings.
Outcome: The proposed system is simple and efficient to reduce maintenance and financial costs and is extensible to support open development and transparency.
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection (2023.emnlp-main)

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Challenge: Existing methods for few-shot cross-lingual transfer learning are limited in target languages due to the scarcity of resources.
Approach: They propose a method which interpolates pairs of instances based on the angle of their representations and propose augmentation methods to enhance few-shot cross-lingual abusive language detection.
Outcome: The proposed method improves few-shot cross-lingual abusive language detection in seven languages typologically distinct from English and three different domains.
ROUGE-K: Do Your Summaries Have Keywords? (2024.starsem-1)

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Challenge: Existing evaluation metrics for extreme summarization models do not pay explicit attention to keywords in summaries, leaving developers ignorant of their presence.
Approach: They propose a keyword-oriented evaluation metric, dubbed ROUGE-K, which quantifies how well summaries include keywords.
Outcome: The proposed model can be extended to include more keywords while keeping the overall quality.
A Survey on Modelling Morality for Text Analysis (2024.findings-acl)

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Challenge: Recent work on modelling morality in text has garnered increasing attention due to its complexity and complexity.
Approach: They provide a systematic review of recent work on modelling morality in text . they discuss challenges and research gaps in the area of NLP .
Outcome: The authors present their work on the modelling of morality in text, which has garnered increasing attention in recent years.
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog (2022.findings-acl)

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Challenge: Recent work shows that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining.
Approach: They propose a resource-efficient and modular domain specialization by means of domain adapters in which domain knowledge is encoded.
Outcome: The proposed framework extracts domain-specific terms and then uses them to build DomainCC and DomainReddit resources based on masked language modeling and response selection objectives.

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GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

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