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AcTED: Automatic Acquisition of Typical Event Duration for Semi-supervised Temporal Commonsense QA


Core Concepts
A novel semi-supervised approach using voting to automatically acquire typical event durations shows significant performance improvements in temporal commonsense QA tasks.
Abstract
Abstract: Proposes a semi-supervised approach to acquire typical event durations using voting. Pseudo labels exhibit high accuracy and coverage. Introduction: Understanding temporal commonsense is crucial in NLP tasks. Weakly supervised approaches struggle with learning typical event durations. Method: Acquiring typical durations through majority voting. Training final model with pseudo-labeled data. Experiments and Discussion: Proposed models show state-of-the-art performance with minimal training data. Comparison with other models and efficiency analysis. Conclusion: Novel semi-supervised method for acquiring typical event durations. Achieves superior results in temporal commonsense QA tasks.
Stats
Using only pseudo examples of 400 events, performance comparable to BERT-based approaches. 7% improvement in Exact Match compared to RoBERTa baselines.
Quotes
"We propose a voting-driven semi-supervised approach to automatically acquire the typical duration of an event." "Our pseudo labels exhibit surprisingly high accuracy and balanced coverage."

Key Insights Distilled From

by Felix Virgo,... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18504.pdf
AcTED

Deeper Inquiries

How can the proposed method be adapted to handle a broader range of temporal commonsense questions?

The proposed method can be adapted to handle a broader range of temporal commonsense questions by expanding the sources from which sentences are sampled to acquire typical event durations. Instead of solely relying on Wikipedia sentences, incorporating data from diverse sources such as news articles, books, or other reliable textual sources can provide a more comprehensive understanding of typical event durations across various contexts. By increasing the variety of sources, the model can learn from a wider range of examples, enhancing its ability to generalize to different types of temporal commonsense questions. Additionally, the method can be extended to consider more complex temporal relationships beyond simple event durations. For instance, incorporating information about event sequences, temporal order, and frequency of events can further enrich the model's understanding of temporal commonsense. By incorporating a more diverse set of temporal features and relationships, the model can be better equipped to handle a broader range of temporal commonsense questions effectively.

What are the potential limitations of relying on Wikipedia sentences for acquiring typical event durations?

While relying on Wikipedia sentences for acquiring typical event durations offers a rich source of textual data, there are several potential limitations to consider: Biased Representation: Wikipedia may have inherent biases in the types of events and durations mentioned in its articles, leading to a skewed representation of typical event durations. This bias can impact the model's ability to generalize to a broader range of temporal commonsense questions. Limited Coverage: Wikipedia may not cover all possible events or durations, leading to gaps in the training data. This limited coverage can result in the model being less effective in handling uncommon or niche temporal commonsense questions. Quality of Data: The quality and accuracy of information in Wikipedia sentences can vary, leading to noise in the training data. Inaccurate or misleading information can negatively impact the model's learning process and performance. Static Data: Wikipedia data is static and may not capture real-time or dynamic changes in typical event durations. Events and their durations can evolve over time, and relying solely on static Wikipedia data may not reflect these changes accurately. Legal and Ethical Considerations: There may be legal and ethical considerations related to using Wikipedia data for training models, such as copyright issues or concerns about data privacy. Considering these limitations, it is essential to supplement Wikipedia data with other sources and validation methods to ensure a more robust and comprehensive training dataset for acquiring typical event durations.

How might the voting-driven approach impact the scalability and generalizability of the model beyond the current dataset?

The voting-driven approach can have significant implications for the scalability and generalizability of the model beyond the current dataset in the following ways: Scalability: The voting-driven approach allows for the automatic acquisition of typical event durations from a large corpus of text data, enabling the model to scale efficiently to handle a vast number of events and sentences. By aggregating predictions and leveraging a voting mechanism, the model can process a high volume of data and extract accurate typical durations without the need for manual annotation, making it scalable to larger datasets. Generalizability: The voting-driven approach enhances the generalizability of the model by capturing the common patterns and characteristics of typical event durations across different contexts. By learning from a diverse set of sentences and events, the model can generalize its understanding of temporal commonsense beyond the specific examples in the training dataset. This generalizability allows the model to perform well on unseen data and adapt to new temporal commonsense questions effectively. Robustness: The majority voting strategy employed in the approach helps improve the robustness of the model by reducing the impact of individual prediction errors. By considering the collective predictions of multiple instances, the model can make more reliable estimations of typical event durations, enhancing its robustness to noise and variability in the data. Overall, the voting-driven approach enhances the scalability and generalizability of the model by efficiently acquiring typical event durations, capturing common temporal patterns, and improving the model's ability to handle diverse temporal commonsense questions effectively.
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