  This paper addressed the problem of structured sentiment analysis using a
bi-affine semantic dependency parser, large pre-trained language models, and
publicly available translation models. For the monolingual setup, we
considered: (i) training on a single treebank, and (ii) relaxing the setup by
training on treebanks coming from different languages that can be adequately
processed by cross-lingual language models. For the zero-shot setup and a given
target treebank, we relied on: (i) a word-level translation of available
treebanks in other languages to get noisy, unlikely-grammatical, but annotated
data (we release as much of it as licenses allow), and (ii) merging those
translated treebanks to obtain training data. In the post-evaluation phase, we
also trained cross-lingual models that simply merged all the English treebanks
and did not use word-level translations, and yet obtained better results.
According to the official results, we ranked 8th and 9th in the monolingual and
cross-lingual setups.
