| Challenge: | Existing studies on article dwell time prediction are noisy and may not show the actual user engagement or satisfaction. |
| Approach: | They propose a deep neural network architecture to extract emotion, event and entity features from an article and learn interactions among them. |
| Outcome: | The proposed model outperforms state-of-the-art models on a real newspaper dataset. |
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| Challenge: | Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available. |
| Approach: | They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. |
| Outcome: | The proposed system outperforms neural and non-neural baselines on multiple real-world datasets. |
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (D18-1)
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| Challenge: | Pre-trained word embeddings provide significant improvements over untrained embeddables . Feature analysis reveals structural patterns of headline popularity . |
| Approach: | They use a multi-task GRU network to model headline popularity . they find that pre-trained word embeddings provide significant improvements over untrained embeddables . |
| Outcome: | The proposed model improves on pre-trained word embeddings and untrained embeddables . it also improves with the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging . |
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)
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| Challenge: | Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule. |
| Approach: | They propose a graph neural network-based model which captures sentence interactions within a document. |
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Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (2020.acl-main)
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| Challenge: | Existing studies have used a manual segmentation of a tweet sequence into equallyspaced intervals based on either tweet counts or time duration. |
| Approach: | They propose to model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. |
| Outcome: | The proposed model predicts the posting time and the stance labels of future tweets more accurately compared to baselines. |
Neural News Recommendation with Heterogeneous User Behavior (D19-1)
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| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
Dynamic Structured Neural Topic Model with Self-Attention Mechanism (2023.findings-acl)
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
| Approach: | They propose a dynamic structured neural topic model which captures topic dependencies while capturing their dependencies. |
| Outcome: | The proposed model outperforms a prior dynamic embedded topic model regarding perplexity and coherence while maintaining sufficient diversity across topics. |
Improving Event Duration Prediction via Time-aware Pre-training (2020.findings-emnlp)
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| Challenge: | Understanding duration of event expressed in text is crucial task in NLP . current methods focus on developing features and cannot utilize external textual knowledge. |
| Approach: | They propose two models that incorporate external knowledge by reading temporal-related news sentences. |
| Outcome: | The proposed models outperform baseline models and capture duration information more accurately. |
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for event-event temporal relation extraction are sparse on event-time information. |
| Approach: | They propose a model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. |
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Conversational Document Prediction to Assist Customer Care Agents (2020.emnlp-main)
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Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using a conversational search system, the agent/system can ask clarification questions and interactively modify the search results as the conversation progresses. |
| Approach: | They propose to use a public dataset to analyze the task of predicting the documents that customer care agents can use to facilitate users’ needs. |
| Outcome: | The proposed model is more efficient than existing models and is more cost-effective than existing ones. |
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model (P18-2)
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| Challenge: | Existing work on predicting popularity of online petitions based on initial popularity trajectory has focused on estimating the number of signatures a petition gets in the first x hours, and predicting the total number of signed petitions at the end of its lifetime. |
| Approach: | They propose a CNN-based model to predict the popularity of a petition based on its textual content and use it to model the influence of other petition signers. |
| Outcome: | The proposed model is based on UK and US government petition datasets and is compared with previous work on predicting popularity over time based upon initial popularity trajectory. |