| Challenge: | Existing methods for detecting events from publicly available data streams such as twitter have been used to model topics from large corpora. |
| Approach: | They propose to use on-line Latent Dirichlet Allocation to model topic shifts and on-lines change point detection algorithms to detect when significant changes occur. |
| Outcome: | The proposed algorithm yields F-scores up to 52% on the detection of real-life changes from social media data streams. |
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| Challenge: | a new method for detecting salient changes from on-line conversations is needed . linguistic preprocessing and time series are used to build a time series . |
| Approach: | They propose a framework for detecting salient changes from on-line conversations . they use linguistic preprocessing to build a time series and change point detection algorithms to detect salient change. |
| Outcome: | The proposed method can detect salient changes in on-line conversations with high accuracy. |
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data (2021.findings-emnlp)
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| Challenge: | Existing methods for generating time series on textual data are not efficient . |
| Approach: | They propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA . they compute similarity of sequentially obtained topic and word distributions over consecutive time periods . |
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Detecting Machine-Generated Long-Form Content with Latent-Space Variables (2024.findings-emnlp)
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| Challenge: | Existing zero-shot methods to distinguish machine-generated long-form texts from humans are vulnerable to domain shift including different decoding strategies, variations in prompts, and attacks. |
| Approach: | They propose a method that incorporates abstract elements as key deciding factors by training a latent-space model on sequences of events or topics derived from human-written texts. |
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Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance. |
| Approach: | They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process. |
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Event-Related Bias Removal for Real-time Disaster Events (2020.findings-emnlp)
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| Challenge: | Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. |
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(Chat)GPT v BERT Dawn of Justice for Semantic Change Detection (2024.findings-eacl)
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| Challenge: | In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. |
| Approach: | They propose to use (Chat)GPT to solve two diachronic extensions of the Word-in-Context task: TempoWiC and HistoWic. |
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MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations (2024.acl-short)
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| Challenge: | a new problem setting is designed to detect critical moments in conversations . a human-annotated multi-modal dataset is used to classify and detect turning points . |
| Approach: | They propose a problem setting focusing on turning points in conversations as TPs . they propose MTPC, MTPD, & MTPR tasks to classify and detect turning points . |
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Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (2023.acl-industry)
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| Challenge: | Existing work on fake news detection does not consider the temporal shift issue caused by the rapidly-evolving nature of news data. |
| Approach: | They propose a framework to forecast temporal patterns of news data and guide detector to fast adapt to future distributions. |
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A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
| Challenge: | Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance . |
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| Outcome: | The proposed model will be able to detect human-written content in real time. |
TP-Detector: Detecting Turning Points in the Engineering Process of Large-scale Projects (2023.emnlp-demo)
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| Challenge: | Extensive experiments demonstrate the effectiveness of our proposed method on a constructed dataset compared to baseline methods. |
| Approach: | They propose a novel task of detecting turning points in the engineering process of large-scale projects by treating news streams as a window with multiple instances. |
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