no code implementations • EMNLP (MRL) 2021 • Riccardo Bassani, Anders Søgaard, Tejaswini Deoskar
This work explores the idea of learning multilingual language models based on clustering of monolingual segments.
no code implementations • 10 Oct 2022 • Sofia Nikiforova, Tejaswini Deoskar, Denis Paperno, Yoad Winter
Our approach includes a novel way of using image location to identify relevant open-domain facts in an external knowledge base, with their subsequent integration into the captioning pipeline at both the encoding and decoding stages.
no code implementations • 25 Jul 2021 • Qianqian Qi, David J. Hessen, Tejaswini Deoskar, Peter G. M. van der Heijden
In this article, we present a theoretical analysis and comparison of the two techniques in the context of document-term matrices.
no code implementations • COLING 2020 • Sofia Nikiforova, Tejaswini Deoskar, Denis Paperno, Yoad Winter
Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge.
1 code implementation • WS 2019 • Konstantinos Kogkalidis, Michael Moortgat, Tejaswini Deoskar
We propose a novel application of self-attention networks towards grammar induction.