In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages. To this end, we exploit continuous semantic text representations and induce a joint multilingual semantic vector spaces to enable supervised learning using manually-coded sentences across different languages. Our experimental results show that classifiers trained on multilingual data yield performance boosts over monolingual topic classification.
In 2017, Glavasˇet al. REF proposed an approach for cross-lingual topical coding of sentences from electoral manifestos using as training data, manually coded manifestos with a total of 77,500 sentences in four languages: English, French, German and Italian.
Cross-Lingual Classification of Topics in Political Texts
NLP+CSS@ACL
History Computer Science