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Variational Self-attention Model for Sentence Representation

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Qiang Zhang, Shangsong Liang, Emine Yilmaz

Computation and Language · Submitted 30 Dec 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experiments on the stance detection task demonstrate the superiority of our method.

how it is stated · asserted 1.00 read the record →

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arXiv ID1812.11559 (version 4)
Pith ID72QAIRKH, Pith's permanent record of this paper and its review
CitationEmine Yilmaz, Qiang Zhang, Shangsong Liang. "Variational Self-attention Model for Sentence Representation." arXiv:1812.11559 (2018). https://pith.science/paper/72QAIRKH
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,772 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.04 · controlled comparison 0.63 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.66 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v4. Results and the main claim are underlined; .

This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. We model the self-attention vector as random variables by imposing a probabilistic distribution. The self-attention mechanism summarizes source information as an attention vector by weighted sum, where the weights are a learned probabilistic distribution. Compared with conventional deterministic counterpart, the stochastic units incorporated by VSAM allow multi-modal attention distributions. Furthermore, by marginalizing over the latent variables, VSAM is more robust against overfitting. Experiments on the stance detection task demonstrate the superiority of our method.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. contribution 0.94 0.90 0.10 asserted 1.00
s1 We model the self-attention vector as random variables by imposing a probabilistic distribution. method 0.98 0.91 0.03 asserted 0.99
s2 The self-attention mechanism summarizes source information as an attention vector by weighted sum, where the weights are a learned probabilistic distribution. method 0.97 0.44 0.05 asserted 1.00
s3 Compared with conventional deterministic counterpart, the stochastic units incorporated by VSAM allow multi-modal attention distributions. contribution 0.50 0.68 0.10 asserted 0.99
s4 Furthermore, by marginalizing over the latent variables, VSAM is more robust against overfitting. contribution 0.36 0.74 0.10 asserted 1.00
s5 Experiments on the stance detection task demonstrate the superiority of our method. result 1.00 0.95 0.77 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v4
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. We model the self-attention vector as random variables by imposing a probabilistic distribution. The self-attention mechanism summarizes source information as an attention vector by weighted sum, where the weights are a learned probabilistic distribution. Compared with conventional deterministic counterpart, the stochastic units incorporated by VSAM allow multi-modal attention distributions. Furthermore, by marginalizing over the latent variables, VSAM is more robust against overfitting. Experiments on the stance detection task demonstrate the superiority of our method.

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