Browsing Research Institutes and Centres by Author "Pasricha, Nivranshu"

Now showing items 1-6 of 6

    • CURED4NLG: A dataset for table-to-text generation 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (University of Galway, 2023)
      We introduce CURED4NLG, a dataset for the task of table-to-text generation focusing on the public health domain. The dataset consists of 280 pairs of tables and documents extracted from weekly epidemiological reports ...

    • Detecting bot behaviour in social media using digital DNA compression 

      Pasricha, Nivranshu; Hayes, Conor (AICS (Artificial Intelligence and Cognitive Science) 2019, 2019-12-05)
      A major challenge faced by online social networks such as Facebook and Twitter is the remarkable rise of fake and automated bot accounts over the last few years. Some of these accounts have been reported to engage in ...

    • Leveraging rule-based machine translation knowledge for under-resourced neural machine translation models 

      Torregrosa, Daniel; Pasricha, Nivranshu; Chakravarth, Bharathi Raja; Masoud, Maraim; Alonso, Juan; Casas, Noe; Arcan, Mihael (NUI Galway, 2019-08-19)
      Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate from source to target language. While this approach grants total ...

    • NUIG-DSI at the WebNLG+ challenge: Leveraging transfer learning for RDF-to-text generation 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2020-12-18)
      This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model ...

    • NUIG-DSI’s submission to the GEM Benchmark 2021 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2021-08-05)
      This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and ...

    • Utilising knowledge graph embeddings for data-to-text generation 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2020-12-18)
      Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their ...