Andrea

Andrea

he/him

PhD student at the University of Trento and Fondazione Bruno Kessler

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Posts

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Blog Post number 1

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portfolio

publications

Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges

Published in Proceedings of the First Workshop on Gender-Inclusive Translation Technologies, 2023

Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models, which have been found to perpetuate gender bias and discrimination. Specifically, we focus on translation from English into Italian, a language pair representative of salient gender-related linguistic transfer problems. To define GNT, we review a selection of relevant institutional guidelines for gender-inclusive language, discuss its scenarios of use, and examine the technical challenges of performing GNT in MT, concluding with a discussion of potential solutions to encourage advancements toward greater inclusivity in MT.

Recommended citation: Andrea Piergentili, Dennis Fucci, Beatrice Savoldi, Luisa Bentivogli, and Matteo Negri. 2023. Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges. In Proceedings of the First Workshop on Gender-Inclusive Translation Technologies, pages 71–83, Tampere, Finland. European Association for Machine Translation.
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Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus

Published in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Gender inequality is embedded in our communication practices and perpetuated in translation technologies. This becomes particularly apparent when translating into grammatical gender languages, where machine translation (MT) often defaults to masculine and stereotypical representations by making undue binary gender assumptions. Our work addresses the rising demand for inclusive language by focusing head-on on gender-neutral translation from English to Italian. We start from the essentials: proposing a dedicated benchmark and exploring automated evaluation methods. First, we introduce GeNTE, a natural, bilingual test set for gender-neutral translation, whose creation was informed by a survey on the perception and use of neutral language. Based on GeNTE, we then overview existing reference-based evaluation approaches, highlight their limits, and propose a reference-free method more suitable to assess gender-neutral translation.

Recommended citation: Andrea Piergentili, Beatrice Savoldi, Dennis Fucci, Matteo Negri, and Luisa Bentivogli. 2023. Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14124–14140, Singapore. Association for Computational Linguistics.
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A Prompt Response to the Demand for Automatic Gender-Neutral Translation

Published in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), 2024

Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.

Recommended citation: Beatrice Savoldi, Andrea Piergentili, Dennis Fucci, Matteo Negri, and Luisa Bentivogli. 2024. A Prompt Response to the Demand for Automatic Gender-Neutral Translation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 256–267, St. Julian’s, Malta. Association for Computational Linguistics.
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Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models

Published in Proceedings of the 25th Annual Conference of the European Association for Machine Translation, 2024

Machine translation (MT) models are known to suffer from gender bias, especially when translating into languages with extensive gendered morphology. Accordingly, they still fall short in using gender-inclusive language, also representative of non-binary identities. In this paper, we look at gender-inclusive neomorphemes, neologistic elements that avoid binary gender markings as an approach towards fairer MT. In this direction, we explore prompting techniques with large language models (LLMs) to translate from English into Italian using neomorphemes. So far, this area has been under-explored due to its novelty and the lack of publicly available evaluation resources. We fill this gap by releasing Neo-GATE, a resource designed to evaluate gender-inclusive en-it translation with neomorphemes. With Neo-GATE, we assess four LLMs of different families and sizes and different prompt formats, identifying strengths and weaknesses of each on this novel task for MT.

Recommended citation: Andrea Piergentili, Beatrice Savoldi, Matteo Negri, and Luisa Bentivogli. 2024. Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation, pages 298--312, Sheffield, United Kingdom. European Association for Machine Translation.
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Automatic Subtitling and Subtitle Compression: FBK at the IWSLT 2024 Subtitling track

Published in Proceedings of the 25th Annual Conference of the European Association for Machine Translation, 2024

The paper describes the FBK submissions to the Subtitling track of the 2024 IWSLT Evaluation Campaign, which covers both the Automatic Subtitling and the Subtitle Compression task for two language pairs: English to German (en-de) and English to Spanish (en-es). For the Automatic Subtitling task, we submitted two systems: i) a direct model, trained in constrained conditions, that produces the SRT files from the audio without intermediate outputs (e.g., transcripts), and ii) a cascade solution that integrates only free-to-use components, either taken off-the-shelf or developed in-house. Results show that, on both language pairs, our direct model outperforms both cascade and direct systems trained in constrained conditions in last year’s edition of the campaign, while our cascade solution is competitive with the best 2023 runs. For the Subtitle Compression task, our primary submission involved prompting a Large Language Model (LLM) in zero-shot mode to shorten subtitles that exceed the reading speed limit of 21 characters per second. Our results highlight the challenges inherent in shrinking out-of-context sentence fragments that are automatically generated and potentially error-prone, underscoring the need for future studies to develop targeted solutions.

Recommended citation: Marco Gaido, Sara Papi, Mauro Cettolo, Roldano Cattoni, Andrea Piergentili, Matteo Negri, and Luisa Bentivogli. 2024. Automatic Subtitling and Subtitle Compression: FBK at the IWSLT 2024 Subtitling track. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 86–96, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.
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GFG-Gender-Fair Generation: A CALAMITA Challenge

Published in Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024), 2024

Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation challenge intends to help shift toward gender-fair language in written communication. The challenge, designed to assess and monitor the recognition and generation of gender-fair language in both mono- and cross-lingual scenarios, includes three tasks: (1) the detection of gendered expressions in Italian sentences, (2) the reformulation of gendered expressions into gender-fair alternatives, and (3) the generation of gender-fair language in automatic translation from English to Italian. The challenge relies on three different annotated datasets: the GFL-it corpus, which contains Italian texts extracted from administrative documents provided by the University of Brescia; GeNTE, a bilingual test set for gender-neutral rewriting and translation built upon a subset of the Europarl dataset; and Neo-GATE, a bilingual test set designed to assess the use of non-binary neomorphemes in Italian for both fair formulation and translation tasks. Finally, each task is evaluated with specific metrics: average of F1-score obtained by means of BERTScore computed on each entry of the datasets for task 1, an accuracy measured with a gender-neutral classifier, and a coverage-weighted accuracy for tasks 2 and 3.

Recommended citation: Simona Frenda, Andrea Piergentili, Beatrice Savoldi, Marco Madeddu, Martina Rosola, Silvia Casola, Chiara Ferrando, Viviana Patti, Matteo Negri, and Luisa Bentivogli. 2024. GFG - Gender-Fair Generation: A CALAMITA Challenge. In Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024). CEUR Workshop Proceedings.
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MAGNET - MAchines GeNErating Translations: A CALAMITA Challenge

Published in Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024), 2024

We propose MAGNET-MAchines GeNErating Translations, a CALAMITA Challenge which aims at testing the ability of large language models (LLMs) in the hot topic of automatic translation, focusing on Italian and English (in both directions) to overcome the marginality with which Italian is considered by the machine translation community. We propose a benchmark composed of two portions with different distribution policies (one free to use, the other not discloseable), allowing to handle data contamination issues. The publicly available section of the benchmark is distributed on Hugging Face, whereas in this report we describe the details of our challenge, including the prompt formats to be used. Additionally, we report the performance of five models, including a LLM and different sized translation models, in terms of four evaluation metrics, whose scores allow an overall evaluation of the quality of the automatically generated translations.

Recommended citation: Mauro Cettolo, Andrea Piergentili, Sara Papi, Marco Gaido, Matteo Negri, and Luisa Bentivogli. 2024. MAGNET - MAchines GeNErating Translations: A CALAMITA Challenge. In Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024). CEUR Workshop Proceedings.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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