Search Results for author: Kevin Seppi

Found 24 papers, 3 papers with code

When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning

1 code implementation ACL 2022 Orion Weller, Kevin Seppi, Matt Gardner

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.

Multi-Task Learning

Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

1 code implementation NAACL 2021 Wilson Fearn, Orion Weller, Kevin Seppi

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis.

Classification Document Classification +3

You Don't Have Time to Read This: An Exploration of Document Reading Time Prediction

no code implementations ACL 2020 Orion Weller, Hildebr, Jordan t, Ilya Reznik, Christopher Challis, E. Shannon Tass, Quinn Snell, Kevin Seppi

Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time.

The rJokes Dataset: a Large Scale Humor Collection

no code implementations LREC 2020 Orion Weller, Kevin Seppi

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

2 code implementations IJCNLP 2019 Orion Weller, Kevin Seppi

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.

Humor Detection

Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models

no code implementations ACL 2019 Varun Kumar, Alison Smith-Renner, Leah Findlater, Kevin Seppi, Jordan Boyd-Graber

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.

Topic Models

Automatic Evaluation of Local Topic Quality

no code implementations ACL 2019 Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, Kevin Seppi

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.

Topic Models

Cross-referencing using Fine-grained Topic Modeling

no code implementations NAACL 2019 Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Emily Hales, Kevin Seppi

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text.

Preprocessor Selection for Machine Learning Pipelines

no code implementations23 Oct 2018 Brandon Schoenfeld, Christophe Giraud-Carrier, Mason Poggemann, Jarom Christensen, Kevin Seppi

Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing.

BIG-bench Machine Learning Hyperparameter Optimization

Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling

no code implementations ACL 2017 Jeffrey Lund, Connor Cook, Kevin Seppi, Jordan Boyd-Graber

We propose combinations of words as anchors, going beyond existing single word anchor algorithms{---}an approach we call {``}Tandem Anchors{''}.

Document Classification Information Retrieval +2

Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings

no code implementations COLING 2016 Paul Felt, Eric Ringger, Kevin Seppi

In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote.

Word Embeddings

Momresp: A Bayesian Model for Multi-Annotator Document Labeling

no code implementations LREC 2014 Paul Felt, Robbie Haertel, Eric Ringger, Kevin Seppi

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.

Document Classification

Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage

no code implementations LREC 2014 Kevin Black, Eric Ringger, Paul Felt, Kevin Seppi, Kristian Heal, Deryle Lonsdale

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.

Lemmatization Morphological Analysis +2

Using Transfer Learning to Assist Exploratory Corpus Annotation

no code implementations LREC 2014 Paul Felt, Eric Ringger, Kevin Seppi, Kristian Heal

We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings.

Management Part-Of-Speech Tagging +1

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