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8 papers · 2 filters
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Kexin Huang, Abhishek Singh, Sitong Chen +4
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data. In this work, we developed a new text representation Clinical XLNet for c…
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann
Large language models can produce powerful contextual representations that lead to improvements across many NLP tasks. Since these models are typically guided by a sequence of lear…
What Syntactic Structures block Dependencies in RNN Language Models?
Ethan Wilcox, Roger Levy, Richard Futrell
Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide n…
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation
Sebastian Gehrmann, Steven Layne, Franck Dernoncourt
Titles of short sections within long documents support readers by guiding their focus towards relevant passages and by providing anchor-points that help to understand the progressi…
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell +2
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical…
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita +3
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental repres…