@article{liu-etal-2021-universal,
    title = "Universal Discourse Representation Structure Parsing",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella  and
      Bos, Johan",
    journal = "Computational Linguistics",
    volume = "47",
    number = "2",
    month = jun,
    year = "2021",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.cl-2.15/",
    doi = "10.1162/coli_a_00406",
    pages = "445--476",
    abstract = "We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce {\ensuremath{\mathbb{U}}}niversal Discourse Representation Theory ({\ensuremath{\mathbb{U}}}DRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The many-to-one approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the one-to-many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages."
}