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KESA enhances sentiment analysis with novel auxiliary tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed KESA, a novel approach to enhance sentiment analysis in pre-trained language models. This method utilizes two auxiliary tasks: sentiment word cloze, which selects correct sentiment words based on overall polarity, and conditional sentiment prediction, which infers polarity from word sentiment. Experiments show that KESA improves upon existing pre-trained models and can be added to current knowledge-enhanced post-trained models, with code and data made available. AI

IMPACT Introduces a lighter-weight method for incorporating sentiment knowledge into language models, potentially improving performance on sentiment analysis tasks.

RANK_REASON The cluster contains an academic paper detailing a new approach for sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

KESA enhances sentiment analysis with novel auxiliary tasks

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The cluster contains an academic paper detailing a new approach for sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Qinghua Zhao, Shuai Ma, Shuo Ren ·

    KESA: A Knowledge Enhanced Approach For Sentiment Analysis

    arXiv:2202.12093v2 Announce Type: replace Abstract: Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. In this paper, we aim to benefit from sentiment know…

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