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Found 16 papers, 1 papers with code
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
Large-scale clinical data is invaluable to driving many computational scientific advances today.
Self-Normalization Properties of Language Modeling
Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function.
The Role of Context Types and Dimensionality in Learning Word Embeddings
We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks.
A Simple Language Model based on PMI Matrix Approximations
Specifically, we show that with minor modifications to word2vec's algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models.
PMI Matrix Approximations with Applications to Neural Language Modeling
The obtained language modeling is closely related to NCE language models but is based on a simplified objective function.
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function
In this paper we define a measure of dependency between two random variables, based on the Jensen-Shannon (JS) divergence between their joint distribution and the product of their marginal distributions.
The Negochat Corpus of Human-agent Negotiation Dialogues
In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain.
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification
In this work, we combine these two approaches to improve low-shot text classification with two novel methods: a simple bag-of-words embedding approach; and a more complex context-aware method, based on the BERT model.
