90 citations · 144 across the 12 of their papers we have counts for
13 papers
Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese
Kurt Micallef, Albert Gatt, Marc Tanti +2
Multilingual language models such as mBERT have seen impressive cross-lingual transfer to a variety of languages, but many languages remain excluded from these models. In this pape…
Face2Text revisited: Improved data set and baseline results
Marc Tanti, Shaun Abdilla, Adrian Muscat +3
Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we develope…
On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning
Marc Tanti, Lonneke van der Plas, Claudia Borg +1
Recent work has shown evidence that the knowledge acquired by multilingual BERT (mBERT) has two components: a language-specific and a language-neutral one. This paper analyses the…
Automated segmentation of microtomography imaging of Egyptian mummies
Marc Tanti, Camille Berruyer, Paul Tafforeau +6
Propagation Phase Contrast Synchrotron Microtomography (PPC-SRCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remai…
On Architectures for Including Visual Information in Neural Language Models for Image Description
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
A neural language model can be conditioned into generating descriptions for images by providing visual information apart from the sentence prefix. This visual information can be in…
Visuallly Grounded Generation of Entailments from Premises
Somaye Jafaritazehjani, Albert Gatt, Marc Tanti
Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the {\em generation} of hypothe…
Transfer learning from language models to image caption generators: Better models may not transfer better
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
When designing a neural caption generator, a convolutional neural network can be used to extract image features. Is it possible to also use a neural language model to extract sente…
Quantifying the amount of visual information used by neural caption generators
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
This paper addresses the sensitivity of neural image caption generators to their visual input. A sensitivity analysis and omission analysis based on image foils is reported, showin…
Pre-gen metrics: Predicting caption quality metrics without generating captions
Marc Tanti, Albert Gatt, Adrian Muscat
Image caption generation systems are typically evaluated against reference outputs. We show that it is possible to predict output quality without generating the captions, based on…
Grounded Textual Entailment
Hoa Trong Vu, Claudio Greco, Aliia Erofeeva +6
Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of poss…
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Albert Gatt, Marc Tanti, Adrian Muscat +6
The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating te…
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be…
Where to put the Image in an Image Caption Generator
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN…