Search Results for author:
Found 17 papers, 13 papers with code
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation
Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows.
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models
We then show the effectiveness of linguistic features when explicitly leveraged by a regression model for predicting sentence complexity and compare its results with the ones obtained by a fine-tuned neural language model.
A Primer on the Inner Workings of Transformer-based Language Models
The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area.
DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers
In recent years, many interpretability methods have been proposed to help interpret the internal states of Transformer-models, at different levels of precision and complexity.
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings.
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences.
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs.
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing (NLP) tasks.
Inseq: An Interpretability Toolkit for Sequence Generation Models
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools.
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
We publicly release the complete dataset including all collected behavioral data, to foster new research on the translation capabilities of NMT systems for typologically diverse languages.
IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation
The T5 model and its unified text-to-text paradigm contributed in advancing the state-of-the-art for many natural language processing tasks.
Contrastive Language-Image Pre-training for the Italian Language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
A dissemination workshop for introducing young Italian students to NLP
We describe and make available the game-based material developed for a laboratory run at several Italian science festivals to popularize NLP among young students.
Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students
To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years.
ArchiMeDe @ DANKMEMES: A New Model Architecture for Meme Detection
We introduce ArchiMeDe, a multimodal neural network-based architecture used to solve the DANKMEMES meme detections subtask at the 2020 EVALITA campaign.
UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations
This work describes a self-supervised data augmentation approach used to improve learning models' performances when only a moderate amount of labeled data is available.
ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
We first test the effectiveness of our approach in a low-resource setting for Italian, evaluating the conditioning for both topic models and gold annotations.
