Nivranshu Pasricha


2021

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NUIG-DSI’s submission to The GEM Benchmark 2021
Nivranshu Pasricha | Mihael Arcan | Paul Buitelaar
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

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 CommonGen. We follow an approach similar to the one described in the GEM benchmark paper where we use the pre-trained T5-base model for our submission. We train this model on additional monolingual data where we experiment with different masking strategies specifically focused on masking entities, predicates and concepts as well as a random masking strategy for pre-training. In our results we find that random masking performs the best in terms of automatic evaluation metrics, though the results are not statistically significantly different compared to other masking strategies.

2020

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Utilising Knowledge Graph Embeddings for Data-to-Text Generation
Nivranshu Pasricha | Mihael Arcan | Paul Buitelaar
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

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 utility for end-to-end approaches in a data-to-text generation task. Our experiments show that using knowledge graph embeddings can yield an improvement of up to 2 – 3 BLEU points for seen categories on the WebNLG corpus without modifying the underlying neural network architecture.

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NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation
Nivranshu Pasricha | Mihael Arcan | Paul Buitelaar
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

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 architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.

2019

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Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models
Daniel Torregrosa | Nivranshu Pasricha | Maraim Masoud | Bharathi Raja Chakravarthi | Juan Alonso | Noe Casas | Mihael Arcan
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks