Viktoriia Chekalina


MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
Viktoriia Chekalina | Anton Razzhigaev | Albert Sayapin | Evgeny Frolov | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition (CITATION) by using a data-specific generalized version of it (CITATION). The generalization of the standard CP-ALS algorithm allows obtaining optimization gradients without a backpropagation mechanism. It reduces the memory needed in training while providing computational benefits. We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.


Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language
Viktoriia Chekalina | Alexander Bondarenko | Chris Biemann | Meriem Beloucif | Varvara Logacheva | Alexander Panchenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.