13 research outputs found
Study of the microbiome of the fluoroacetate producing plant Dichapetalum cymosum: Investigation of microbial community
Sparse and Structured Visual Attention
Visual attention mechanisms are widely used in multimodal tasks, as visual
question answering (VQA). One drawback of softmax-based attention mechanisms is
that they assign some probability mass to all image regions, regardless of
their adjacency structure and of their relevance to the text. In this paper, to
better link the image structure with the text, we replace the traditional
softmax attention mechanism with two alternative sparsity-promoting
transformations: sparsemax, which is able to select only the relevant regions
(assigning zero weight to the rest), and a newly proposed Total-Variation
Sparse Attention (TVmax), which further encourages the joint selection of
adjacent spatial locations. Experiments in VQA show gains in accuracy as well
as higher similarity to human attention, which suggests better
interpretability
Automated Fact Checking in the News Room
Fact checking is an essential task in journalism; its importance has been
highlighted due to recently increased concerns and efforts in combating
misinformation. In this paper, we present an automated fact-checking platform
which given a claim, it retrieves relevant textual evidence from a document
collection, predicts whether each piece of evidence supports or refutes the
claim, and returns a final verdict. We describe the architecture of the system
and the user interface, focusing on the choices made to improve its
user-friendliness and transparency. We conduct a user study of the
fact-checking platform in a journalistic setting: we integrated it with a
collection of news articles and provide an evaluation of the platform using
feedback from journalists in their workflow. We found that the predictions of
our platform were correct 58\% of the time, and 59\% of the returned evidence
was relevant
Jointly Extracting and Compressing Documents with Summary State Representations
We present a new neural model for text summarization that first extracts
sentences from a document and then compresses them. The proposed model offers a
balance that sidesteps the difficulties in abstractive methods while generating
more concise summaries than extractive methods. In addition, our model
dynamically determines the length of the output summary based on the gold
summaries it observes during training and does not require length constraints
typical to extractive summarization. The model achieves state-of-the-art
results on the CNN/DailyMail and Newsroom datasets, improving over current
extractive and abstractive methods. Human evaluations demonstrate that our
model generates concise and informative summaries. We also make available a new
dataset of oracle compressive summaries derived automatically from the
CNN/DailyMail reference summaries