Papers by Kevin Seppi
You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction (2020.acl-main)
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Orion Weller, Jordan Hildebrandt, Ilya Reznik, Christopher Challis, E. Shannon Tass, Quinn Snell, Kevin Seppi
| Challenge: | Existing work on reading time prediction has focused on word level only predictions . however, previous work has focused only on word levels . |
| Approach: | They perform an experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. |
| Outcome: | The proposed method combines a large number of machine learning methods with textual and stylistic factors to predict the time it takes to read. |
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types (C18-1)
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| Challenge: | Annotated corpora are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. |
| Approach: | They propose a framework for learning from rich prior knowledge to combine annotations with different structures. |
| Outcome: | The proposed model compares favorably with previous work and enables active sample selection to reduce annotation effort. |
Automatic Evaluation of Local Topic Quality (P19-1)
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Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, Kevin Seppi
| Challenge: | Topic models are evaluated with global topic distributions but without local topic assignments. |
| Approach: | They propose a task to elicit human judgments of token-level topic assignments . they propose to use global metrics to evaluate topic models at a local level . |
| Outcome: | The proposed task elicits human judgments of token-level topic assignments . global metrics agree poorly with human assignments, the authors show . |
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning (2022.acl-short)
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| Challenge: | Transfer learning (TL) in natural language processing has seen a surge of interest in recent years . pre-trained models have shown impressive ability to transfer to novel tasks . |
| Approach: | They compare two different methods of transfer learning in natural language processing to find out which is better. |
| Outcome: | The proposed methods perform better when the target task has fewer instances than the supporting task and vice versa. |
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (D18-1)
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| Challenge: | Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm . |
| Approach: | They propose an interactive supervised topic model based on the anchor words algorithm . they propose a classifier which requires no training beyond topic inference . |
| Outcome: | The proposed model is human-interpretable and fast, and can be interactive. |
Humor Detection: A Transformer Gets the Last Laugh (D19-1)
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| Challenge: | Existing methods to identify humor in text have been limited to identifying humor in the text. |
| Approach: | They propose a model that learns to identify humorous jokes based on Reddit ratings, and employ a Transformer architecture to learn from sentence context. |
| Outcome: | The proposed model outperforms previous work on humor identification tasks with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset. |
Cross-referencing Using Fine-grained Topic Modeling (N19-1)
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| Challenge: | Cross-referencing is a useful study aid for facilitating comprehension of a text, but it requires extensive thematic knowledge and a focused search through the corpus to find such useful connections. |
| Approach: | They propose a system for producing candidate cross-references which can be easily verified by human annotators. |
| Outcome: | a new system can produce cross-references that can be easily verified by human annotators . the system uses fine-grained topic modeling to identify verse pairs which are topically related . |
The rJokes Dataset: a Large Scale Humor Collection (2020.lrec-1)
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| Challenge: | Humor is a complex language phenomenon that depends upon many factors, including topic, date, and recipient. |
| Approach: | They compile a large scale humor dataset from the Reddit r/Jokes subreddit. |
| Outcome: | The proposed dataset provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. |
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification (2021.naacl-main)
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| Challenge: | Many text classification algorithms depend on the size of the corpus’ vocabulary due to their bag-of-words representation. |
| Approach: | They propose to evaluate how preprocessing techniques affect the run-time of models by evaluating ten techniques over four models and two datasets. |
| Outcome: | The proposed methods can reduce run-time with no loss of accuracy while sacrificing up to 65%. |
Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models (P19-1)
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| Challenge: | Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics, but with less control. |
| Approach: | They propose to use constraints and informed prior-based methods to improve user control and topic coherence. |
| Outcome: | The proposed methods improve user control and topic coherence, while constraints yield higher quality topics, but with less control. |