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Found 12 papers, 3 papers with code
Shared Task in Evaluating Accuracy: Leveraging Pre-Annotations in the Validation Process
Our submission consists in fact of two submissions; we first analyze solely the performance of the rules and classifiers (pre-annotations), and then the human evaluation aided by the former pre-annotations using the web interface (hybrid).
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models
Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different length distribution.
Preventing RNN from Using Sequence Length as a Feature
Recurrent neural networks are deep learning topologies that can be trained to classify long documents.
Multinational Address Parsing: A Zero-Shot Evaluation
While these models yield notable results, previous work on neural networks has only focused on parsing addresses from a single source country.
Geographic ratemaking with spatial embeddings
This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model.
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them.
Representation Learning
Applications
Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations
Plumitifs (dockets) were initially a tool for law clerks.
Leveraging Subword Embeddings for Multinational Address Parsing
We propose an approach in which we employ subword embeddings and a Recurrent Neural Network architecture to build a single model capable of learning to parse addresses from multiple countries at the same time while taking into account the difference in languages and address formatting systems.
Attending Form and Context to Generate Specialized Out-of-VocabularyWords Representations
We propose a new contextual-compositional neural network layer that handles out-of-vocabulary (OOV) words in natural language processing (NLP) tagging tasks.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.
Predicting and interpreting embeddings for out of vocabulary words in downstream tasks
We propose a novel way to handle out of vocabulary (OOV) words in downstream natural language processing (NLP) tasks.
