• Bilingual contexts from comparable corpora to mine for translations of collocations

      Taslimipoor, Shiva; Mitkov, Ruslan; Corpas Pastor, Gloria; Fazly, Afsaneh (Springer, 2018-03-21)
      Due to the limited availability of parallel data in many languages, we propose a methodology that benefits from comparable corpora to find translation equivalents for collocations (as a specific type of difficult-to-translate multi-word expressions). Finding translations is known to be more difficult for collocations than for words. We propose a method based on bilingual context extraction and build a word (distributional) representation model drawing on these bilingual contexts (bilingual English-Spanish contexts in our case). We show that the bilingual context construction is effective for the task of translation equivalent learning and that our method outperforms a simplified distributional similarity baseline in finding translation equivalents.
    • Bridging the gap: attending to discontinuity in identification of multiword expressions

      Rohanian, Omid; Taslimipoor, Shiva; Kouchaki, Samaneh; Ha, Le An; Mitkov, Ruslan (Association for Computational Linguistics, 2019-06-05)
      We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both, through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.
    • Cross-lingual transfer learning and multitask learning for capturing multiword expressions

      Taslimipoor, Shiva; Rohanian, Omid; Ha, Le An (Association for Computational Linguistics, 2019-08-31)
      Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems. In this study, we explore for the first time, the application of transfer learning (TRL) and multitask learning (MTL) to the identification of Multiword Expressions (MWEs). For MTL, we exploit the shared syntactic information between MWE and dependency parsing models to jointly train a single model on both tasks. We specifically predict two types of labels: MWE and dependency parse. Our neural MTL architecture utilises the supervision of dependency parsing in lower layers and predicts MWE tags in upper layers. In the TRL scenario, we overcome the scarcity of data by learning a model on a larger MWE dataset and transferring the knowledge to a resource-poor setting in another language. In both scenarios, the resulting models achieved higher performance compared to standard neural approaches.
    • Detecting semantic difference: a new model based on knowledge and collocational association

      Taslimipoor, Shiva; Corpas Pastor, Gloria; Rohanian, Omid; Corpas Pastor, Gloria; Colson, Jean-Pierre (John Benjamins Publishing Company, 2020-05-08)
      Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, airplane and car, the word flying can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.
    • GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks

      Taslimipoor, Shiva; Rohanian, Omid; Može, Sara (Association for Computational Linguistics, 2019-06-06)
      This paper describes the system submitted to the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with UCCA’. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.
    • Identification of multiword expressions: A fresh look at modelling and evaluation

      Taslimipoor, Shiva; Rohanian, Omid; Mitkov, Ruslan; Fazly, Afsaneh; Markantonatou, Stella; Ramisch, Carlos; Savary, Agata; Vincze, Veronika (Language Science Press, 2018-10-25)
    • Language resources for Italian: Towards the development of a corpus of annotated Italian multiword expressions

      Taslimipoor, Shiva; Desantis, Anna; Cherchi, Manuela; Mitkov, Ruslan; Monti, Johanna (ceur-ws, 2016-12-05)
      This paper describes the first resource annotated for multiword expressions (MWEs) in Italian. Two versions of this dataset have been prepared: the first with a fast markup list of out-of-context MWEs, and the second with an in-context annotation, where the MWEs are entered with their contexts. The paper also discusses annotation issues and reports the inter-annotator agreement for both types of annotations. Finally, the results of the first exploitation of the new resource, namely the automatic extraction of Italian MWEs, are presented.
    • Using gaze data to predict multiword expressions

      Rohanian, Omid; Taslimipoor, Shiva; Yaneva, Victoria; Ha, Le An (INCOMA Ltd, 2017-09-01)
      In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena. In this paper we conduct a preliminary study towards the automatic identification of multiword expressions based on gaze features from native and non-native speakers of English. We report comparisons between a part-ofspeech (POS) and frequency baseline to: i) a prediction model based solely on gaze data and ii) a combined model of gaze data, POS and frequency. In spite of the challenging nature of the task, best performance was achieved by the latter. Furthermore, we explore how the type of gaze data (from native versus non-native speakers) affects the prediction, showing that data from the two groups is discriminative to an equal degree. Finally, we show that late processing measures are more predictive than early ones, which is in line with previous research on idioms and other formulaic structures.
    • Verbal multiword expressions for identification of metaphor

      Rohanian, Omid; Rei, Marek; Taslimipoor, Shiva; Ha, Le (ACL, 2020-07-06)
      Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first “MWE-aware” metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.
    • What matters more: the size of the corpora or their quality? The case of automatic translation of multiword expressions using comparable corpora.

      Mitkov, Ruslan; Taslimipoor, Shiva (John Benjamins, 2020-05-08)
      This study investigates (and compares) the impact of the size and the similarity/quality of comparable corpora on the specific task of extracting translation equivalents of verb-noun collocations from such corpora. The comprehensive evaluation of different configurations of English and Spanish corpora sheds some light on the more general and perennial question: what matters more – the quantity or quality of corpora?
    • WLV at SemEval-2018 task 3: Dissecting tweets in search of irony

      Rohanian, Omid; Taslimipoor, Shiva; Evans, Richard; Mitkov, Ruslan (Association for Computational Linguistics, 2018-06-05)
      This paper describes the systems submitted to SemEval 2018 Task 3 “Irony detection in English tweets” for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.
    • Wolves at SemEval-2018 task 10: Semantic discrimination based on knowledge and association

      Taslimipoor, Shiva; Rohanian, Omid; Ha, Le An; Corpas Pastor, Gloria; Mitkov, Ruslan (Association for Computational Linguistics, 2018-06)
      This paper describes the system submitted to SemEval 2018 shared task 10 ‘Capturing Discriminative Attributes’. We use a combination of knowledge-based and co-occurrence features to capture the semantic difference between two words in relation to an attribute. We define scores based on association measures, ngram counts, word similarity, and ConceptNet relations. The system is ranked 4th (joint) on the official leaderboard of the task.