| Challenge: | Clickbait is a term used to describe posts intended to entice readers to visit a web page . clickbait spoiling is generating a short text that satisfies the curiosity induced by a clickbaiting post . |
| Approach: | They propose to use clickbait spoiling to generate a short text that satisfies curiosity . they classify the type of spoiler needed and generate appropriate spoilers . |
| Outcome: | The proposed method outperforms all other methods in generating spoilers for both types of clickbait posts. |
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Know Better – A Clickbait Resolving Challenge (2022.lrec-1)
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| Challenge: | a clickbait headline or teaser is used to "bait" the reader into clicking a link to an article . clickbaiting is annoying but effective, and can be countered with specialized models . |
| Approach: | They propose to construct approaches that can automatically extract relevant information from clickbait articles . they argue that clickbaiting can probably not be defeated with clickbaitting detection alone . |
| Outcome: | The proposed methods outperform question answering models on clickbait resolving task . the data will be used to give users tools to counter clickbaiting in the future . |
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference (2024.emnlp-main)
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| Challenge: | a new method to detect clickbait posts on the Web is needed to detect such posts. |
| Approach: | They propose a method to detect clickbait posts on the Web using latent factors . they use features in multiple modalities to characterize the posts and causal inference to eliminate noise . |
| Outcome: | The proposed method can detect clickbait posts on popular social media platforms with good generalization ability. |
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
| Approach: | They investigate how much models benefit from retrieving text passages . they obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks ." |
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Predicting Clickbait Strength in Online Social Media (2020.coling-main)
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| Challenge: | Clickbaits are sensational, provocative or controversial posts that entice readers to click on them. |
| Approach: | They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity. |
| Outcome: | The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts. |
Crowdsourcing a Large Corpus of Clickbait on Twitter (C18-1)
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Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Erika Patricia Garces Fernandez, Matthias Hagen, Benno Stein
| Challenge: | Clickbait is a nuisance on social media. |
| Approach: | a corpus of 38,517 annotated Twitter tweets was constructed to detect clickbait . the corpus was annotating tweets on 4-point scale by five annotators at Amazon's Mechanical Turk . |
| Outcome: | The corpus of 38,517 annotated Twitter tweets was used to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017 . |
Leveraging Structured Metadata for Improving Question Answering on the Web (2020.aacl-main)
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| Challenge: | Using metadata information from web pages can improve the performance of answer passage selection/reranking models. |
| Approach: | They propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy to learn the representation for metadata predicates in a hierarchical way. |
| Outcome: | The proposed model outperforms baseline models on the MS MARCO and Recipe-MARCO datasets and shows that it is more accurate than baseline models. |
Knowing More About Questions Can Help: Improving Calibration in Question Answering (2021.findings-acl)
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| Challenge: | Existing work on calibration focuses on model confidence, such as the max probability of the predicted class. |
| Approach: | They propose a calibration method which estimates whether model correctly predicts answer for each question. |
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Open-Domain Question Answering (2020.acl-tutorials)
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
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Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation (2024.emnlp-main)
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| Challenge: | Objective questions such as fill-in-the-blank and multiple-choice require examinees to select one valid answer from a set of invalid options. |
| Approach: | They examine distractor generation tasks, datasets, methods, and evaluation metrics for English objective questions. |
| Outcome: | The proposed task is based on fill-in-the-blank and multiple choice questions and is widely utilized in educational settings across various domains and subjects. |
Leveraging Context Information for Natural Question Generation (N18-2)
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| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
| Approach: | They propose a model that matches the answer with the passage before generating a question. |
| Outcome: | The proposed model outperforms the state-of-the-art model using rich features. |