InDeep × NMT:Empowering Human Translators via Interpretable Neural Machine Translation

Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process

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. While some multilingual variants of the T5 model have recently been introduced, their performances were found to provide suboptimal performances for languages other than English if compared to monolingual variants. We are motivated by these findings to introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We perform a thorough cleaning of a web-crawled Italian corpus including more than 40 billion words and use it to pretrain three IT5 models of different sizes. The performance of IT5 models and their multilingual counterparts is then evaluated on a broad range of natural language understanding and generation benchmarks for Italian. We find the monolingual IT5 models to provide the best scale-to-performance ratio across tested models, consistently outperforming their multilingual counterparts and setting a new state-of-the-art for most Italian conditional language generation tasks.Comment: 13 pages, 7 tables, 1 figure. Code and checkpoints available: https://github.com/gsarti/it

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. However, the questions of when and which parts of the context affect model generations are typically tackled separately, with current plausibility evaluations being practically limited to a handful of artificial benchmarks. To address this, we introduce Plausibility Evaluation of Context Reliance (PECoRe), an end-to-end interpretability framework designed to quantify context usage in language models' generations. Our approach leverages model internals to (i) contrastively identify context-sensitive target tokens in generated texts and (ii) link them to contextual cues justifying their prediction. We use \pecore to quantify the plausibility of context-aware machine translation models, comparing model rationales with human annotations across several discourse-level phenomena. Finally, we apply our method to unannotated model translations to identify context-mediated predictions and highlight instances of (im)plausible context usage throughout generation.Comment: ICLR 2024 Camera Ready. Code: https://github.com/gsarti/pecore. Artifacts: https://huggingface.co/collections/gsarti/pecore-iclr-2024-65edab42e28439e21b612c2