Papers by Olga Majewska
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning (2020.emnlp-main)
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| Challenge: | XCOPA dataset provides a typologically diverse dataset for commonsense reasoning in 11 languages . current methods for evaluating commonsensible reasoning in resource-poor languages are weak compared to translation-based transfer. |
| Approach: | They propose a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. |
| Outcome: | The proposed model performs better than current methods on a resource-poor dataset compared to translation-based transfer in the 11 languages studied . |
Acquiring Verb Classes Through Bottom-Up Semantic Verb Clustering (L18-1)
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| Challenge: | Existing methods for creating verbal classifications are limited or non-existent in most languages . a range of automatic verb classification approaches have been proposed, but high-quality resources are needed . |
| Approach: | They propose to use top-up semantic clustering to extract syntactic and semantic information from verbs in English, Polish and Croatian. |
| Outcome: | The proposed classifications in English, Polish and Croatian are compared with other languages. |
Verb Knowledge Injection for Multilingual Event Processing (2021.acl-long)
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| Challenge: | Recent studies have shown that pretrainers implicitly extract a non-negligible amount of linguistic knowledge from text corpora in an unsupervised fashion. |
| Approach: | They propose to inject explicit verb knowledge into dedicated adapter modules to complement the linguistic knowledge obtained during LM-pretraining. |
| Outcome: | The proposed model improves in English event extraction tasks, while injecting verb knowledge improves other languages. |
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction (2020.lrec-1)
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Olga Majewska, Diana McCarthy, Jasper van den Bosch, Nikolaus Kriegeskorte, Ivan Vulić, Anna Korhonen
| Challenge: | Existing methods for creating large-scale semantic similarity resources are slow and expensive . a large verb similarity dataset is available for a number of verbs, but not for English. |
| Approach: | They propose a method for fast bottom-up creation of large-scale semantic similarity resources . they leverage semantic intuitions of native speakers and adapt a spatial multi-arrangement approach to lexical stimuli. |
| Outcome: | The proposed approach produces a large-scale verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs. |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis (2020.coling-main)
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| Challenge: | Existing methods to learn general language representations from large volumes of unlabeled text have been used to improve multilingual NLP. |
| Approach: | They propose to use a spatial arrangement method to generate large-scale evaluation datasets that balance cross-lingual alignment with language specificity. |
| Outcome: | The proposed method produces semantic verb classes and fine-grained similarity scores for nearly 130 thousand verb pairs. |
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation (2023.tacl-1)
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| Challenge: | Multilingual task-oriented dialogue (ToD) datasets suffer from severe limitations, such as being small in scale and lacking naturalness and cultural specificity in the target language. |
| Approach: | They propose a novel outline-based annotation process where domain-specific abstract schemata of dialogue are mapped into natural language outlines. |
| Outcome: | The proposed approach improves understanding, dialogue state tracking, and end-to-end dialogue evaluation in Arabic, Indonesian, Russian, and Kiswahili. |
Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems - with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. |
| Outcome: | This tutorial will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems. |