Papers by Simone Ponzetto
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. |