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Efficient Purely Convolutional Text Encoding

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Szymon Malik, Adrian Lancucki, Jan Chorowski

Computation and Language · Submitted 3 Aug 2018

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

The authors' main claim Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings.

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arXiv ID1808.01160 (version 1)
Pith ID75CEOYP7, Pith's permanent record of this paper and its review
CitationAdrian Lancucki, Jan Chorowski, Szymon Malik. "Efficient Purely Convolutional Text Encoding." arXiv:1808.01160 (2018). https://pith.science/paper/75CEOYP7
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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, 4,188 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.33 · human participants 0.03 · controlled comparison 0.65 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.91 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences. Such representations are useful for building NLP systems, including conversational agents. Our work derives from a recently proposed recursive convolutional architecture for auto-encoding text paragraphs at byte level. We propose alternations that significantly reduce training time, the number of parameters, and improve auto-encoding accuracy. Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings.

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 In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences. contribution 0.73 0.85 0.08 asserted 0.97
s1 Such representations are useful for building NLP systems, including conversational agents. background 0.94 0.07 0.04 asserted 0.95
s2 Our work derives from a recently proposed recursive convolutional architecture for auto-encoding text paragraphs at byte level. background 0.95 0.37 0.03 asserted 0.97
s3 We propose alternations that significantly reduce training time, the number of parameters, and improve auto-encoding accuracy. contribution 0.77 0.94 0.22 asserted 1.00
s4 Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings. result 0.96 0.96 0.76 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.90.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • 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

In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences. Such representations are useful for building NLP systems, including conversational agents. Our work derives from a recently proposed recursive convolutional architecture for auto-encoding text paragraphs at byte level. We propose alternations that significantly reduce training time, the number of parameters, and improve auto-encoding accuracy. Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Uses the method or tool of 1 paper
Cites as background 1 paper
  • Active research of different architectures led to great advances and eventually a shift towards methods using the transformer architecture [ref] or convolutional layers [ref] among researchers and practitioners alike.

    Convolutional Sequence to Sequence Learning · Introduction · 0.85

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