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Sentence

3,811 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28

Playing Games

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 3,811 papers with code (10,752 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.

  • 1 Sep 2014 124 repositories listed Syntology ran 21 of 44 samples · 23 unverified · 16 pointer-only (licence)
    Neural machine translation is a recently proposed approach to machine translation.
  • 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.
  • 17 Nov 2014 74 repositories listed Syntology ran 13 of 34 samples · 21 unverified · 6 pointer-only (licence)
    Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.
  • 10 Sep 2014 74 repositories listed Syntology ran 11 of 25 samples · 14 unverified · 9 pointer-only (licence)
    Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
  • 28 May 2020 67 repositories listed Syntology ran 15 of 65 samples · 50 unverified · 4 pointer-only (licence)
    By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
  • 27 Aug 2019 64 repositories listed Syntology ran 20 of 58 samples · 38 unverified · 11 pointer-only (licence)
    However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference…
  • 9 Mar 2017 52 repositories listed Syntology ran 4 of 10 samples · 6 unverified · 2 pointer-only (licence)
    This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
  • 26 Sep 2019 48 repositories listed Syntology ran 46 of 126 samples · 80 unverified · 22 pointer-only (licence)
    Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
  • 17 Aug 2015 44 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 2 pointer-only (licence)
    Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25.
  • 26 Sep 2016 28 repositories listed Syntology ran 23 of 46 samples · 23 unverified · 12 pointer-only (licence)
    To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
  • 9 Aug 2015 25 repositories listed Syntology ran 0 of 8 samples · 8 unverified
    It can also use sentence level tag information thanks to a CRF layer.
  • 29 Mar 2018 24 repositories listed Syntology ran 1 of 22 samples · 21 unverified · 1 pointer-only (licence)
    For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
  • 20 Nov 2014 24 repositories listed Syntology ran 12 of 32 samples · 20 unverified · 27 pointer-only (licence)
    We propose a novel paradigm for evaluating image descriptions that uses human consensus.
  • 18 Apr 2021 23 repositories listed Syntology ran 17 of 30 samples · 13 unverified · 19 pointer-only (licence)
    This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
  • 5 May 2017 23 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
    Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
  • 21 Apr 2019 20 repositories listed Syntology ran 44 of 76 samples · 32 unverified · 33 pointer-only (licence)
    We propose BERTScore, an automatic evaluation metric for text generation.
  • 21 Sep 2016 20 repositories listed Syntology ran 4 of 15 samples · 11 unverified · 7 pointer-only (licence)
    Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing.
  • 22 Aug 2019 19 repositories listed Syntology ran 7 of 21 samples · 14 unverified
    For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained…
  • 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).
  • 19 Jun 2015 18 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 3 pointer-only (licence)
    We find that this straightforward model can generate simple conversations given a large conversational training dataset.
  • 19 Nov 2015 17 repositories listed Syntology ran 13 of 31 samples · 18 unverified · 19 pointer-only (licence)
    The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation.
  • 28 Feb 2019 16 repositories listed Syntology ran 9 of 32 samples · 23 unverified · 3 pointer-only (licence)
    Intent classification and slot filling are two essential tasks for natural language understanding.
  • 22 Jun 2015 16 repositories listed Syntology ran 0 of 3 samples · 3 unverified
    The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.
  • 9 Nov 2015 15 repositories listed
    The basic objective of this paper is to reach the same results using reinforcement learning with general function approximators that can be achieved by using the classical Q lookup table on small input samples.
  • 16 Jun 2018 14 repositories listed
    Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques.
  • 20 Apr 2018 14 repositories listed
    Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability…
  • 31 Oct 2017 14 repositories listed Syntology ran 3 of 14 samples · 11 unverified · 5 pointer-only (licence)
    By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.
  • 26 Dec 2018 13 repositories listed Syntology ran 4 of 10 samples · 6 unverified · 4 pointer-only (licence)
    We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
  • 5 Nov 2016 13 repositories listed Syntology ran 0 of 15 samples · 15 unverified
    Lipreading is the task of decoding text from the movement of a speaker's mouth.
  • 27 Feb 2021 12 repositories listed Syntology ran 16 of 24 samples · 8 unverified · 5 pointer-only (licence)
    In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer…

Syntology lines on 27 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.

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