1 code implementation • 30 May 2019 • Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh
We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment.
1 code implementation • ACL 2020 • Emanuele Bugliarello, Naoaki Okazaki
Most neural machine translation models only rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism.
1 code implementation • ACL 2020 • Emanuele Bugliarello, Sabrina J. Mielke, Antonios Anastasopoulos, Ryan Cotterell, Naoaki Okazaki
The performance of neural machine translation systems is commonly evaluated in terms of BLEU.
3 code implementations • 30 Nov 2020 • Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki, Desmond Elliott
Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing.
1 code implementation • EACL 2021 • Emanuele Bugliarello, Desmond Elliott
Image captioning has focused on generalizing to images drawn from the same distribution as the training set, and not to the more challenging problem of generalizing to different distributions of images.
4 code implementations • EMNLP 2021 • Stella Frank, Emanuele Bugliarello, Desmond Elliott
Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality.
1 code implementation • CoNLL (EMNLP) 2021 • Heather Lent, Emanuele Bugliarello, Miryam de Lhoneux, Chen Qiu, Anders Søgaard
Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature.
3 code implementations • EMNLP 2021 • Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti, Siva Reddy, Nigel Collier, Desmond Elliott
The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet.
Ranked #1 on Zero-Shot Cross-Lingual Transfer on MaRVL
3 code implementations • 27 Jan 2022 • Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulić
Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups.
no code implementations • ACL 2022 • Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages.
no code implementations • 22 Apr 2022 • Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas
We consider the task of sequencing tracks on music streaming platforms where the goal is to maximise not only user satisfaction, but also artist- and platform-centric objectives, needed to ensure long-term health and sustainability of the platform.
no code implementations • 24 May 2022 • Aishwarya Agrawal, Ivana Kajić, Emanuele Bugliarello, Elnaz Davoodi, Anita Gergely, Phil Blunsom, Aida Nematzadeh
Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).
no code implementations • insights (ACL) 2022 • Heather Lent, Emanuele Bugliarello, Anders Søgaard
We aim to learn language models for Creole languages for which large volumes of data are not readily available, and therefore explore the potential transfer from ancestor languages (the 'Ancestry Transfer Hypothesis').
1 code implementation • 14 Jul 2022 • Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
We pretrain the 86M parameter PIXEL model on the same English data as BERT and evaluate on syntactic and semantic tasks in typologically diverse languages, including various non-Latin scripts.
Ranked #1 on Named Entity Recognition (NER) on MasakhaNER
1 code implementation • 24 Oct 2022 • Chen Qiu, Dan Oneata, Emanuele Bugliarello, Stella Frank, Desmond Elliott
We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model.
Zero-Shot Cross-Lingual Image-to-Text Retrieval Zero-Shot Cross-Lingual Text-to-Image Retrieval +3
1 code implementation • 30 Mar 2023 • Lucas Beyer, Bo Wan, Gagan Madan, Filip Pavetic, Andreas Steiner, Alexander Kolesnikov, André Susano Pinto, Emanuele Bugliarello, Xiao Wang, Qihang Yu, Liang-Chieh Chen, Xiaohua Zhai
A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well.
2 code implementations • 12 May 2023 • Emanuele Bugliarello, Laurent Sartran, Aishwarya Agrawal, Lisa Anne Hendricks, Aida Nematzadeh
While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained models lack "fine-grained" understanding, such as the ability to recognise relationships, verbs, and numbers in images.
Ranked #13 on Visual Reasoning on Winoground
1 code implementation • 23 May 2023 • Emanuele Bugliarello, Aida Nematzadeh, Lisa Anne Hendricks
Recent work in vision-and-language pretraining has investigated supervised signals from object detection data to learn better, fine-grained multimodal representations.
no code implementations • 25 Oct 2023 • Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis
In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable.
1 code implementation • 26 Oct 2023 • Laura Cabello, Emanuele Bugliarello, Stephanie Brandl, Desmond Elliott
We quantify bias amplification in pretraining and after fine-tuning on three families of vision-and-language models.
1 code implementation • 3 Apr 2024 • Constanza Fierro, Nicolas Garneau, Emanuele Bugliarello, Yova Kementchedjhieva, Anders Søgaard
Facts are subject to contingencies and can be true or false in different circumstances.
no code implementations • 25 Apr 2024 • Olivia Wiles, Chuhan Zhang, Isabela Albuquerque, Ivana Kajić, Su Wang, Emanuele Bugliarello, Yasumasa Onoe, Chris Knutsen, Cyrus Rashtchian, Jordi Pont-Tuset, Aida Nematzadeh
Human-rated prompt sets are generally small and the reliability of the ratings -- and thereby the prompt set used to compare models -- is not evaluated.