Summary of An Information-theoretic Approach to Analyze Nlp Classification Tasks, by Luran Wang et al.
An Information-Theoretic Approach to Analyze NLP Classification Tasks
by Luran Wang, Mark Gales, Vatsal Raina
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an information-theoretic framework for analyzing the influence of inputs on output in text classification tasks. It provides a model to understand how different components of input texts affect the prediction outcome, which is crucial for various NLP applications. The framework is applied to multiple-choice reading comprehension and sentiment classification, revealing interesting insights about the role of context and semantic meaning in these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how text inputs influence the output in language-based tasks like reading comprehension and sentiment analysis. It shows that context plays a more significant role than questions on challenging datasets, and this matters when designing multiple-choice questions for assessments. The study also finds that the semantic meaning of input texts dominates over linguistic realizations in determining sentiment. |
Keywords
» Artificial intelligence » Classification » Nlp » Text classification
