Most of the existing work focuses primarily on the monoplex setting where we have access to a network with only a single type of connection between entities.
Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text.
Our study of 22 mitigation techniques and five baselines reveals up to 12. 6% fairness variance across identical training runs with identical seeds.
We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task.
Organizations around the world face an array of risks impacting their operations globally.
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.
Ranked #1 on Question Answering on FinQA
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.
One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities.
Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems.
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects.
Social and Information Networks
In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data.
This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs.
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter.