no code implementations • WMT (EMNLP) 2020 • Inigo Jauregi Unanue, Massimo Piccardi
This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS_NLP) team for the WMT20 English-Basque biomedical translation tasks.
1 code implementation • WMT (EMNLP) 2020 • Rachel Bawden, Giorgio Maria Di Nunzio, Cristian Grozea, Inigo Jauregi Unanue, Antonio Jimeno Yepes, Nancy Mah, David Martinez, Aurélie Névéol, Mariana Neves, Maite Oronoz, Olatz Perez-de-Viñaspre, Massimo Piccardi, Roland Roller, Amy Siu, Philippe Thomas, Federica Vezzani, Maika Vicente Navarro, Dina Wiemann, Lana Yeganova
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
no code implementations • INLG (ACL) 2020 • Federico Betti, Giorgia Ramponi, Massimo Piccardi
In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation.
no code implementations • 28 Mar 2024 • Nhu Vo, Dat Quoc Nguyen, Dung D. Le, Massimo Piccardi, Wray Buntine
Machine translation for Vietnamese-English in the medical domain is still an under-explored research area.
1 code implementation • 20 Mar 2024 • Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi
In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest.
no code implementations • 16 Jan 2024 • Tom Roth, Inigo Jauregi Unanue, Alsharif Abuadbba, Massimo Piccardi
Current adversarial attack algorithms, where an adversary changes a text to fool a victim model, have been repeatedly shown to be effective against text classifiers.
1 code implementation • 8 Jun 2023 • Inigo Jauregi Unanue, Gholamreza Haffari, Massimo Piccardi
Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/few-shots cross-lingual transfer).
1 code implementation • 19 Sep 2022 • Artur Grigorev, Adriana-Simona Mihaita, Khaled Saleh, Massimo Piccardi
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
1 code implementation • ACL 2022 • 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.
no code implementations • ACL (spnlp) 2021 • 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.
no code implementations • ACL 2021 • Inigo Jauregi Unanue, Jacob Parnell, Massimo Piccardi
Neural machine translation models are often biased toward the limited translation references seen during training.
no code implementations • COLING 2020 • Inigo Jauregi Unanue, Nazanin Esmaili, Gholamreza Haffari, Massimo Piccardi
Document-level machine translation focuses on the translation of entire documents from a source to a target language.
no code implementations • 8 Jul 2020 • Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming.
no code implementations • 30 Sep 2019 • Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
This is a serious issue for low-resource language pairs and many specialized translation domains that are inherently limited in the amount of available supervised data.
no code implementations • NAACL 2019 • Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Nazanin Esmaili, Massimo Piccardi
Regularization of neural machine translation is still a significant problem, especially in low-resource settings.
no code implementations • ALTA 2018 • Hanieh Poostchi, Massimo Piccardi
Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection.
1 code implementation • WS 2018 • Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators.
1 code implementation • 29 Jun 2017 • Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.
1 code implementation • COLING 2016 • Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources.
1 code implementation • 25 Nov 2016 • Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research.
1 code implementation • WS 2016 • Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi
Extraction of concepts present in patient clinical records is an essential step in clinical research.
1 code implementation • WS 2016 • Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi
Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline.
no code implementations • 30 Jul 2015 • Shaukat Abidi, Massimo Piccardi, Mary-Anne Williams
In the proposed approach, the action class is predicted by a structural model(learnt by Latent Structural SVM) based on measurements from the image superpixels and their latent classes.
no code implementations • 10 Mar 2015 • Ava Bargi, Richard Yi Da Xu, Massimo Piccardi
This infinite adaptive online approach is capable of segmenting and classifying the sequential data over unlimited number of classes, while meeting the memory and delay constraints of streaming contexts.
no code implementations • 2 Jul 2013 • Ava Bargi, Richard Yi Da Xu, Massimo Piccardi
In this paper, we propose a non-parametric conditional factor regression (NCFR)model for domains with high-dimensional input and response.