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pro vyhledávání: '"Attanasio Giuseppe"'
The automatic assessment of translation quality has recently become crucial across several stages of the translation pipeline, from data curation to training and decoding. Although quality estimation (QE) metrics have been optimized to align with hum
Externí odkaz:
http://arxiv.org/abs/2410.10995
The translation of gender-neutral person-referring terms (e.g., the students) is often non-trivial. Translating from English into German poses an interesting case -- in German, person-referring nouns are usually gender-specific, and if the gender of
Externí odkaz:
http://arxiv.org/abs/2406.06131
Autor:
Raus Rachele, Tonti Michela, Cerquitelli Tania, Cagliero Luca, Attanasio Giuseppe, La Quatra Moreno, Greco Salvatore
Publikováno v:
SHS Web of Conferences, Vol 138, p 01007 (2022)
Cet article présente le projet E-MIMIC, une application qui vise à éliminer les préjugés et la non-inclusion dans les textes administratifs rédigés dans les pays européens, à commencer par ceux qui sont rédigés dans les langues romanes. Il
Externí odkaz:
https://doaj.org/article/8ddbeba0591a4970871e0f542180ece1
Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But w
Externí odkaz:
http://arxiv.org/abs/2403.04445
Current automatic speech recognition (ASR) models are designed to be used across many languages and tasks without substantial changes. However, this broad language coverage hides performance gaps within languages, for example, across genders. Our stu
Externí odkaz:
http://arxiv.org/abs/2402.17954
Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling fairnes
Externí odkaz:
http://arxiv.org/abs/2310.12127
Autor:
Bianchi, Federico, Suzgun, Mirac, Attanasio, Giuseppe, Röttger, Paul, Jurafsky, Dan, Hashimoto, Tatsunori, Zou, James
Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate harmf
Externí odkaz:
http://arxiv.org/abs/2309.07875
Recent advances in eXplainable AI (XAI) have provided new insights into how models for vision, language, and tabular data operate. However, few approaches exist for understanding speech models. Existing work focuses on a few spoken language understan
Externí odkaz:
http://arxiv.org/abs/2309.07733
Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-dom
Externí odkaz:
http://arxiv.org/abs/2309.02311
Autor:
Röttger, Paul, Kirk, Hannah Rose, Vidgen, Bertie, Attanasio, Giuseppe, Bianchi, Federico, Hovy, Dirk
Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful an
Externí odkaz:
http://arxiv.org/abs/2308.01263