Machine Translation | Gabriele Sarti

Machine Translation

Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement

We evaluate unsupervised word-level quality estimation (WQE) methods for machine translation, focusing on their robustness to human label variation.

Steering Large Language Models for Machine Translation Personalization

We evaluate prompting and steering based methods for machine translation personalization in the literary domain.

QE4PE: Word-level Quality Estimation for Human Post-Editing

We investigate the impact of word-level quality estimation on MT post-editing with 42 professional post-editors.

DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers

We propose DecoderLens, a method to interpret the iterative refinement of representations in encoder-decoder Transformer models.

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

We introduce PECoRe, an interpretability framework for identifying context dependence in language model generations.

RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation

We introduce Retrieval and Attribute-Marking enhanced Prompting (RAMP) to perform attribute-controlled MT with multilingual LLMs.

Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation

We analyze input contributions of char-level MT models and show how they modulate word and character-level information.

Inseq: An Interpretability Toolkit for Sequence Generation Models

We present Inseq, a Python library to democratize access to interpretability analyses of sequence generation models.

DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages

DivEMT is a publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.