7 citations · 8 across the 5 of their papers we have counts for
8 papers · 1 filter
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models.…
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation
Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has b…
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
Inigo Jauregi Unanue, Jacob Parnell, Massimo Piccardi
Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-…
Leveraging Discourse Rewards for Document-Level Neural Machine Translation
Inigo Jauregi Unanue, Nazanin Esmaili, Gholamreza Haffari +1
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translat…
Learning Neural Textual Representations for Citation Recommendation
Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham +2
With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several appr…
Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation
Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
In recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from ove…