Sentence Classification

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

14 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 115 papers with code (303 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  11 Oct 2018 534 repositories listed Syntology ran 204 of 659 samples · 455 unverified · 149 pointer-only (licence)
  We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
• Convolutional Neural Networks for Sentence Classification
  25 Aug 2014 118 repositories listed Syntology ran 19 of 77 samples · 58 unverified · 15 pointer-only (licence)
  We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
  25 Jan 2019 19 repositories listed Syntology ran 4 of 25 samples · 21 unverified · 1 pointer-only (licence)
  Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
• A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
  13 Oct 2015 19 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
• PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
  17 Oct 2017 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  First, the majority of datasets for sequential short-text classification (i.
• SciBERT: A Pretrained Language Model for Scientific Text
  26 Mar 2019 6 repositories listed
  Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
• What you can cram into a single vector: Probing sentence embeddings for linguistic properties
  3 May 2018 6 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
• Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
  15 Dec 2016 5 repositories listed
  Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.
• Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings
  13 Mar 2023 3 repositories listed
  Training and inference on edge devices often requires an efficient setup due to computational limitations.
• QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
  25 Feb 2021 3 repositories listed
  Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
• IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
  11 Sep 2020 3 repositories listed Syntology ran 2 of 14 samples · 12 unverified
  Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in the natural language processing (NLP) is slow-moving due to a lack of available…
• CLUE: A Chinese Language Understanding Evaluation Benchmark
  13 Apr 2020 3 repositories listed
  The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
• Augmenting Data with Mixup for Sentence Classification: An Empirical Study
  22 May 2019 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the…
• ListOps: A Diagnostic Dataset for Latent Tree Learning
  17 Apr 2018 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models.
• Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
  21 Sep 2016 3 repositories listed
  Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision.
• Neural Semantic Encoders
  14 Jul 2016 3 repositories listed
  We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.
• How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning
  5 Feb 2024 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We investigate the mechanism of in-context learning (ICL) on sentence classification tasks with semantically-unrelated labels ("foo"/"bar").
• Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs
  22 May 2023 2 repositories listed
  ColexNet's nodes are concepts and its edges are colexifications.
• Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models
  22 Oct 2022 2 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks.
• Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers
  12 Jul 2021 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings.
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
  15 Jun 2021 2 repositories listed Syntology ran 4 of 16 samples · 12 unverified
  Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
• MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
  18 Apr 2021 2 repositories listed
  Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks.
• Voice@SRIB at SemEval-2020 Task 9 and 12: Stacked Ensembling method for Sentiment and Offensiveness detection in Social Media
  20 Jul 2020 2 repositories listed
  The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation.
• GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples
  1 Jul 2020 2 repositories listed
  Recent Transformer-based architectures, e.
• CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
  30 Apr 2020 2 repositories listed
  This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern.
• On Dimensional Linguistic Properties of the Word Embedding Space
  5 Oct 2019 2 repositories listed
  Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.
• Investigating an Effective Character-level Embedding in Korean Sentence Classification
  31 May 2019 2 repositories listed
  Different from the writing systems of many Romance and Germanic languages, some languages or language families show complex conjunct forms in character composition.
• Taming Pretrained Transformers for Extreme Multi-label Text Classification
  7 May 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
• Glyce: Glyph-vectors for Chinese Character Representations
  29 Jan 2019 2 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be…
• Jointly Learning to Label Sentences and Tokens
  14 Nov 2018 2 repositories listed
  Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size.

Syntology lines on 15 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.