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Found 24 papers, 3 papers with code
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning
We find that there is a simple heuristic for when to use one of these techniques over the other: pairwise MTL is better than STILTs when the target task has fewer instances than the supporting task and vice versa.
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification
Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis.
You Don't Have Time to Read This: An Exploration of Document Reading Time Prediction
Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time.
Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer
Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor.
The rJokes Dataset: a Large Scale Humor Collection
We also introduce this dataset as a task for future work, where models learn to predict the level of humor in a joke.
Humor Detection: A Transformer Gets the Last Laugh
These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93. 1% for the Puns dataset and 98. 6% on the Short Jokes dataset.
Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments.
Cross-referencing using Fine-grained Topic Modeling
Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text.
Automatic Evaluation of Local Topic Quality
Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments.
Preprocessor Selection for Machine Learning Pipelines
Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing.
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013).
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
Annotated corpora enable supervised machine learning and data analysis.
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling
We propose combinations of words as anchors, going beyond existing single word anchor algorithms{---}an approach we call {``}Tandem Anchors{''}.
Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings
In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote.
Fast Inference for Interactive Models of Text
Probabilistic models are a useful means for analyzing large text corpora.
Using Transfer Learning to Assist Exploratory Corpus Annotation
We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings.
Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
The task of corpus-dictionary linkage (CDL) is to annotate each word in a corpus with a link to an appropriate dictionary entry that documents the sense and usage of the word.
Momresp: A Bayesian Model for Multi-Annotator Document Labeling
We introduce MomResp, a model that incorporates information from both natural data clusters as well as annotations from multiple annotators to infer ground-truth labels and annotator reliability for the document classification task.
First Results in a Study Evaluating Pre-annotation and Correction Propagation for Machine-Assisted Syriac Morphological Analysis
Manual annotation of large textual corpora can be cost-prohibitive, especially for rare and under-resourced languages.
