activity
20152020
most citedReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

7 citations · 7 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2020

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…

cs.CL2020

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…

cs.CL2019

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…

cs.CL20197 cited

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Nazanin Esmaili +1

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (Re…

cs.CL2018

A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems

Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi

Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) a…

stat.ML2015

An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data

Ava Bargi, Richard Yi Da Xu, Massimo Piccardi

In the recent years, the desire and need to understand sequential data has been increasing, with particular interest in sequential contexts such as patient monitoring, understandin…