3 citations · 5 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…