Mehrnoush ShamsFard

Dilara Torunoğlu-Selamet, Dogukan Arslan, Rodrigo Wilkens et al.

Potentially idiomatic expressions (PIEs) construe meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows to evaluate model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.

Zeinab Rahimi, Mehrnoush ShamsFard

Recognizing causal elements and causal relations in text is one of the challenging issues in natural language processing; specifically, in low resource languages such as Persian. In this research we prepare a causality human annotated corpus for the Persian language which consists of 4446 sentences and 5128 causal relations and three labels of cause, effect and causal mark -- if possibl -- are specified for each relation. We have used this corpus to train a system for detecting causal elements boundaries. Also, we present a causality detection benchmark for three machine learning methods and two deep learning systems based on this corpus. Performance evaluations indicate that our best total result is obtained through CRF classifier which has F-measure of 0.76 and the best accuracy obtained through Bi-LSTM-CRF deep learning method with Accuracy equal to %91.4.