Search Results for author:
Found 11 papers, 6 papers with code
Cross-Domain Image Captioning with Discriminative Finetuning
However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner without finetuning.
Can discrete information extraction prompts generalize across language models?
We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information.
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
Referential communication in heterogeneous communities of pre-trained visual deep networks
As a first step in this direction, we systematically explore the task of \textit{referential communication} in a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates.
Communication breakdown: On the low mutual intelligibility between human and neural captioning
We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner.
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN
We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task.
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important.
Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game
Research in multi-agent cooperation has shown that artificial agents are able to learn to play a simple referential game while developing a shared lexicon.
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks
Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of "jump around" 0-shot from the component words.
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018
This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018.
