# Zero-shot Cross-Linguistic Learning of Event Semantics

arXiv: https://arxiv.org/abs/2207.02356 · HTML: https://mlsanity.com/paper/2207.02356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures](https://mlsanity.com/paper/1601.03896/) (2016)
- [Morphological Analyzer and Generator for Russian and Ukrainian Languages](https://mlsanity.com/paper/1503.07283/) (2015)
- [Clue: Cross-modal Coherence Modeling for Caption Generation](https://mlsanity.com/paper/2005.00908/) (2020)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [CITE: A Corpus of Image-Text Discourse Relations](https://mlsanity.com/paper/1904.06286/) (2019)
- [Temporal and Aspectual Entailment](https://mlsanity.com/paper/1904.01297/) (2019)
- [Incorporating Temporal Information in Entailment Graph Mining](https://mlsanity.com/paper/2109.09412/) (2021)
- [Image Specificity](https://mlsanity.com/paper/1502.04569/) (2015)
- [Aspectuality Across Genre: A Distributional Semantics Approach](https://mlsanity.com/paper/2011.00345/) (2020)

## Abstract (arXiv)

Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face. In this paper, we look specifically at captions of images across Arabic, Chinese, Farsi, German, Russian, and Turkish and describe a computational model for predicting lexical aspects. Despite the heterogeneity of these languages, and the salient invocation of distinctive linguistic resources across their caption corpora, speakers of these languages show surprising similarities in the ways they frame image content. We leverage this observation for zero-shot cross-lingual learning and show that lexical aspects can be predicted for a given language despite not having observed any annotated data for this language at all.