Papers by Ana C Farinha

2 papers
MT-Telescope: An interactive platform for contrastive evaluation of MT systems (2021.acl-demo)

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Challenge: MT-Telescope is an open source, written in Python, and is built around a user friendly and dynamic web interface.
Approach: They propose a platform to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems.
Outcome: The proposed platform supports fine-grained segment-level analysis and interactive visualisations that expose the fundamental differences in the performance of the compared systems.
COMET: A Neural Framework for MT Evaluation (2020.emnlp-main)

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Challenge: Historically, metrics for evaluating the quality of machine translation (MT) have relied on basic, lexical-level features such as counting the number of matching n-grams between the MT hypothesis and the reference translation.
Approach: They propose a neural framework for training multilingual machine translation evaluation models which exploits human judgements to obtain new state-of-the-art levels of correlation with MT quality.
Outcome: The proposed framework achieves state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.

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