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Found 12 papers, 2 papers with code
Semi-automatically Annotated Learner Corpus for Russian
We present ReLCo— the Revita Learner Corpus—a new semi-automatically annotated learner corpus for Russian.
Applying Gamification Incentives in the Revita Language-learning System
Our goal is to keep the learner engaged in long practice sessions over many months—rather than for the short-term.
Projecting named entity recognizers without annotated or parallel corpora
Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models.
What do Transformers Know about Government?
This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models. In particular, we explore how BERT encodes the government relation between constituents in a sentence.
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data.
Effects of sub-word segmentation on performance of transformer language models
Lastly, we show 4. that LMs of smaller size using morphological segmentation can perform comparably to models of larger size trained with BPE -- both in terms of (1) perplexity and (3) scores on downstream tasks.
Linguistic Constructs as the Representation of the Domain Model in an Intelligent Language Tutoring System
This paper presents the development of an AI-based language learning platform Revita.
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions.
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors.
Modeling language learning using specialized Elo rating
This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.
Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences.
Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size.
