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.

本文使用双仿射语义依赖分析器、大型预训练语言模型和公开可用的翻译模型解决了结构化情感分析的问题。对于单语言环境，作者考虑了在单个语料库上训练和跨语言模型上下文下训练。对于零样本场景，作者通过单词级别的翻译和合并训练数据来处理目标语料库。在后期评估阶段，作者还训练了交叉语言模型，而不是使用单词级别的翻译，并获得了更好的结果。