From the Stage to the Audience: Propaganda on Reddit

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 394–404 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 394 Bootstrapping Multilingual AMR with Contextual Word Alignments Janaki Sheth**∗ Young-Suk Lee* Ram´on Fernandez Astudillo* Tahira Naseem* Radu Florian* Salim Roukos* Todd Ward* **Perelman School of Medicine, UPenn, Philadelphia, PA, USA *IBM Research, Yorktown Heights, NY, USA Janaki.Sheth@Pennmedicine.upenn.edu, ramon.astudillo@ibm.com {ysuklee, tnaseem, raduf, roukos, toddward}@us.ibm.com Abstract We develop high performance multilingual Abstract Meaning Representation (AMR) sys- tems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer- based multilingual word embeddings, in partic- ular those from cross-lingual RoBERTa (XLM- R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between En- glish and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese. 1 Introduction Abstract Meaning Representation graphs are rooted, labeled, directed, acyclic graphs repre- senting sentence-level semantics (Banarescu et al., 2013). In the example shown in Figure 1, the sen- tence The boy wants to go is parsed into an AMR graph. The nodes of the AMR graph represent the AMR concepts, which may include normal- ized surface symbols e.g. boy, Propbank frames (Kingsbury and Palmer, 2002) e.g. want-01, go-02 as well as other AMR-specific constructs. Edges in an AMR graph represent the relations between concepts. In this example :arg0, :arg1 correspond to standard roles of Propbank. One distinctive aspect of AMR annotation is the lack of explicit alignments between nodes in the graph and words in the sentences. Since such align- ments are essential for training many of present- day AMR parsers, there have been various efforts to link the AMR concepts to their corresponding span of words (Flanigan et al., 2014; Pourdamghani ∗ This research was done during an internship at IBM Research AI. Figure 1: AMR graph for The boy wants to go and its German translation Der Junge will gehen. Implicit alignments between the English text and AMR con- cepts are denoted by dotted arrows. Explicit alignments between English and German texts are denoted by solid arrows. et al., 2014; Lyu and Titov, 2018; Chen and Palmer, 2017). A significant emphasis of this paper is on deriving these alignments for multilingual AMR parsers. Even though by nature AMR is biased towards English, recent work has evaluated the potential of AMR to work as an interlingua. Hajiˇc et al. (2014) and Xue et al. (2014) categorize and propose refine- ments for divergences in the annotation between English and Chinese as well as Czech AMRs. An- chiˆeta and Pardo (2018) import the correspond- ing AMR annotation for each sentence from the English annotated corpus and revisit the annota- tion to adapt it to Portuguese. However, Damonte and Cohen (2018) show that it may be possible to use the original AMR annotations …

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