Pith. sign in

REVIEW 3 major objections 5 minor 56 references

Message Passing Attention Networks for Document Understanding

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that representing documents as directed word co-occurrence graphs and processing them with message passing attention networks yields text classification accuracy competitive with state-of-the-art sequence models.

desk verdict A real architectural contribution with honest ablations, but the 'competitive' claim is under-supported by borrowed baselines and missing variance estimates. read the letter →

arxiv 1908.06267 v2 pith:62ONDGEZ submitted 2019-08-17 cs.CL

Giannis Nikolentzos , Antoine J.-P. Tixier , Michalis Vazirgiannis This is my paper · ORCID
classification cs.CL
keywords messagepassingnetworksgraphneuraltextclassificationdocumentunderstandingwordco-occurrencegraphsself-attentionhierarchicalmodelsinductivelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a document can be treated as a directed, weighted word co-occurrence graph, and that a graph neural network built on the message passing framework can classify text at state-of-the-art levels. It introduces MPAD, which passes messages between words co-occurring within a two-word window, updates node representations with a gated recurrent unit, and reads out by self-attention combined with a special document-level master node. Evaluated on ten standard text classification datasets, the paper reports that MPAD reaches the best accuracy on five of them, and that adding explicit sentence-level hierarchy yields the best MPAD variant on nine of ten. If the claim holds, it shows that word-order-sensitive graph representations are a viable alternative to recurrent and convolutional text encoders.

What carries the argument

The central object is the directed weighted word co-occurrence network of a document, together with the message passing update loop defined on it. The aggregate step computes $M^{t+1} = \mathrm{MLP}^{t+1}(D^{-1} A H^t)$, a degree-normalized weighted average of neighbor features; the combine step is a GRU update $H^{t+1} = \mathrm{GRU}(H^t, M^{t+1})$; and the readout applies global self-attention over word nodes, concatenates the special master node's vector as a skip connection, and concatenates these readouts across all $t=1,\dots,T$. This mechanism lets node features encode word-level, bigram-level, and higher compositional information while the master node accumulates a document summary.

What would settle it

Re-run CNN, DAN, HN-ATT, DiSAN, and the other baselines on the same ten datasets under MPAD's exact preprocessing and validation protocol and report per-seed error bars; if several datasets where MPAD leads by less than a point flip or become statistically tied, the paper's 'competitive with state-of-the-art' conclusion would not survive.

Watch

Extended reading notes

Core claim

At the paper's center is the claim that the message passing paradigm transfers to document understanding when documents are encoded as per-document word co-occurrence networks. Each unique word is a node; directed edges follow the text with a sliding window of size two, and edge weights are co-occurrence counts; a master node connects to every word. Message passing aggregates neighbor features through a layer-specific MLP after row-normalizing the adjacency matrix, then combines them with a GRU so nodes retain memory across iterations. The final graph representation concatenates a self-attentional pooling of all word nodes with the master node's vector, and this readout is applied at every iteration and concatenated, mixing local and global features. On ten datasets spanning topic, sentiment, and subjectivity classification, the authors report their architectures as competitive with the state of the art, with hierarchical variants (sentence-level graphs combined by attention, a sentence clique, or a sentence path) improving over vanilla MPAD on nine of ten datasets.

Load-bearing premise

The load-bearing premise is that the baseline accuracies, most of which are quoted from the original papers rather than re-run under the same preprocessing, validation splits, and hyperparameter settings as MPAD, are directly comparable; with differences below one point on some datasets, a fair re-run could change the ranking.

Editorial extensions

If this is right

  • A purely graph-based text encoder can match sequence models such as CNN and hierarchical attention networks on standard benchmarks, without convolution or recurrent sentence encoders.
  • Making the document graph directed matters: on sentiment datasets, undirected edges hurt performance, consistent with the need to model word order for negation.
  • Reading out at every message passing iteration is necessary for deeper models; with only the final-iteration readout, more than two iterations decreases accuracy.
  • Explicitly hierarchical variants help on most datasets, but not uniformly; single-document graphs capture cross-sentence context that isolated sentence encoders miss.
  • MPAD is inductive and its cost does not depend on vocabulary size, unlike transductive graph-text methods that must see all test documents and compute pairwise word statistics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test of the paper's core hypothesis would isolate the contribution of the master node: the ablation shows removing it hurts everywhere, suggesting the design could transfer to other graph classification tasks where a global summary node is omitted.
  • The directed-edge design suggests a natural extension to tasks where word order is decisive, such as natural language inference or fact verification, where the single-graph encoding could be compared against sequence transformers at similar parameter counts.
  • The baseline comparison could be made decisive by re-running every baseline under the same preprocessing, validation, and random-seed protocol and reporting confidence intervals; several reported gaps are under one point.
  • Because co-occurrence graphs grow with document length, an implicit corollary is that MPAD's memory footprint scales with vocabulary per document, so long-document applications may need graph sparsification or hierarchical chunking.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes MPAD, a message passing graph neural network for document classification. Documents are represented as directed, weighted word co-occurrence graphs with a special master node; message passing uses an MLP aggregate and a GRU combine, and the readout concatenates self-attention over all nodes with the master node's final representation, applied at every message passing iteration. Three hierarchical variants (MPAD-sentence-att, MPAD-clique, MPAD-path) encode documents sentence-by-sentence and then combine sentence representations. The authors evaluate on 10 text classification datasets and report that MPAD reaches the best accuracy on 5 of 10 datasets and that hierarchical variants outperform vanilla MPAD on 9 of 10 datasets, with ablations on Reuters, Polarity, and IMDB examining the number of iterations, edge direction, the master node, renormalization, the GRU combine, and the skip connection. Public code is provided.

Significance. If the empirical claims hold, the paper makes a modest but useful contribution: it demonstrates that a message passing architecture over per-document co-occurrence graphs, with careful design choices (master node, multi-readout, GRU combine), is a viable inductive text classifier, and it provides an explicit comparison with transductive graph-based text classification. The architecture is described in sufficient detail to be reproduced, and the release of code, the breadth of datasets, and the systematic ablations are genuine strengths. The central limitation is that the headline 'competitive with the state-of-the-art' claim rests on comparisons whose statistical and protocol comparability is not established; the reported margins on several datasets are smaller than typical run-to-run variation.

major comments (3)
  1. [Section 5.1, Table 2] The load-bearing claim that MPAD is competitive with the state of the art, and specifically 'best on 5 of 10 datasets', is not supported with adequate evidence. The text states that baseline scores are 'reported in the original papers' and that 'some of the baselines' were re-evaluated, but it does not say which rows were re-run, under which preprocessing, or under which validation protocol. Several decisive margins are very small: MPQA 90.12 vs 90.10, Reuters 97.07 vs 97.21 (in favor of the baseline), SST-2 88.30 vs 87.8, and TREC 95.60 vs 94.6. No standard deviations, error bars, or significance tests are reported for any row, including MPAD. A shift of a few tenths of a point, well within normal variance for these datasets, would change the 'best on 5/10' count. Please identify exactly which baselines were re-run, report variance estimates, or carry out significance tests; without this, the central comparison cannot be adjudicated.
  2. [Table 1 and Section 4.3] The cross-validation protocol is underspecified. Table 1 marks BBCSport, Polarity, Subjectivity, and MPQA as 'CV', but the number of folds is never stated, and Section 4.3 only says that a validation set is formed by randomly sampling 10% of the training set of each fold. No random seeds or number of repeated runs are reported. This matters particularly for BBCSport, which has only 737 training examples, and for the small margins in Table 2. Please specify the fold count, the seed(s), and whether the reported accuracies are averages over multiple runs.
  3. [Section 5.2, Table 3] The statement that 'having more iterations improves performance' is stronger than the data in Table 3 supports. From 1 to 2 iterations there is a consistent improvement, but later iterations are non-monotonic: on Reuters 2MP and 3MP both give 97.07 while 4MP gives 97.48; on Polarity 3MP (80.20) is worse than 2MP (80.24); on IMDB 2MP, 3MP, and 4MP all give 91.30. The claim should be restricted to the effect of going from 1 to 2 iterations, or the authors should explain why non-monotonicity is expected under the multi-readout design.
minor comments (5)
  1. [Table 3] The row 'MPAD 2MP no master node skip connection' reads '96.9380.62 91.12'; there is a missing space between 96.93 and 80.62.
  2. [Table 3 caption] The phrase 'The n innMP refers to the number of message passing iterations' contains a typo; it should read 'The n in MP'.
  3. [Section 3.1] The phrase 'a sliding window of size 2 overspanning sentences' is ungrammatical; it should be 'spanning sentences' or 'overlapping sentences'.
  4. [Section 4.2] For the WMD baseline, the description says 'A k-nearest neighbor classifier is used', but no value of k is given; if this follows the original paper, a citation to the specific configuration would be helpful.
  5. [Section 5.1] The claim that the 5 datasets on which MPAD ranks first 'widely differ in training set size, number of categories, and prediction task' would be more informative if the corresponding rows in Table 2 were explicitly referenced, since TREC, Reuters, and SST-2 are not obvious from the table without counting.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: MPAD's claims rest on external benchmark evaluations, not on self-referential definitions or fitted predictions.

full rationale

The paper's central claim is empirical: MPAD and its hierarchical variants are evaluated on 10 standard text classification datasets against published and re-run baselines. The architecture is defined independently of the benchmark labels: word co-occurrence graphs are built from documents, the message passing, vertex update, and readout components are specified explicitly, and parameters are optimized by minimizing cross-entropy on training and validation splits. No quantity reported as a result is defined in terms of the fitted parameters; accuracies are computed on held-out test sets. Hyperparameter selection by validation accuracy is standard practice, not a circular step. The only self-citations (e.g., the SPGK baseline and related graph-kernel work) are baselines or contextual references and are not used to justify the validity of the proposed model. The paper does not invoke a self-citation to exclude alternatives, and it does not import a uniqueness theorem from the authors' prior work. That some baseline numbers are taken from original papers affects the strength of the comparative claim, but that is an evidence-comparability concern, not circular reasoning.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical or mathematical entities are postulated; the master node is an architectural component with ablation support, and all other components are drawn from prior literature. The main free choices are hyperparameters and the graph construction window size.

free parameters (4)
  • Number of message passing iterations T = 2 (basic MPAD and each hierarchical level)
    Set to 2 by default; ablation in Table 3 shows 1-4 iterations give 96.57-97.48 on Reuters, so the choice is not uniquely determined by data.
  • Hidden dimension d = 64 (128 on IMDB and Yelp2013)
    Manual hyperparameter choice, not systematically tuned; affects model capacity.
  • Word co-occurrence sliding window size = 2
    Graph construction parameter fixed in Section 3.1; not ablated, so its effect on results is unknown.
  • Dropout rate = 0.5
    Standard regularization hyperparameter, fixed across all experiments.
assumptions (4)
  • domain assumption A sliding window of size 2 over sentences creates a word co-occurrence graph that retains enough word-order information for document classification.
    Section 3.1 defines the graph using this window; the direction ablation shows order matters, but the window size itself is not varied.
  • domain assumption Baseline accuracy scores from cited papers are directly comparable to MPAD's results.
    Section 5.1 compares to Table 2 scores taken from original publications without re-running under identical preprocessing and validation conditions.
  • domain assumption Pretrained 300-dimensional Google News word vectors provide a useful initialization for all non-Yelp datasets.
    Section 4.3 states these embeddings initialize H0; the model does not learn word representations from scratch on those datasets.
  • domain assumption The GRU-based COMBINE function can represent both local and global signals across message passing iterations.
    Section 3.2 interprets the GRU this way; it is an architectural premise validated only by the reported accuracy and ablations.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Message Passing Attention Networks for Document Understanding." pith.science (2026). https://pith.science/paper/62ONDGEZ

@misc{pith2026190806267,
  author       = {Pith},
  title        = {Pith review of: Message Passing Attention Networks for Document Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/62ONDGEZ}},
  note         = {Machine review of arXiv:1908.06267}
}
read the original abstract

Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad .

Figures

Figures reproduced from arXiv: 1908.06267 by the authors.

Figure 1
Figure 1. Illustration of MPAD-path (}: master node). Yang et al. 2016), and spoken language understanding (Ra￾heja and Tetreault 2019; Shang et al. 2019). Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows. In all of them, we repre￾sent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously describ… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 46 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

  2. [2]

    Atwood, J., and Towsley, D. 2016. Diffusion-Convolutional Neural Networks . In Advances in Neural Information Processing Systems , 1993--2001

    work page 2016

  3. [3]

    J.; et al

    Battaglia, P.; Pascanu, R.; Lai, M.; Rezende, D. J.; et al. 2016. Interaction Networks for Learning about Objects, Relations and Physics . In Advances in Neural Information Processing Systems , 4502--4510

    work page 2016

  4. [4]

    Cheng, J.; Dong, L.; and Lapata, M. 2016. Long Short-Term Memory-Networks for Machine Reading . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 551--561

    work page 2016

  5. [5]

    Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014. Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation . In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing , 1724--1734

    work page 2014

  6. [6]

    Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling . arXiv preprint arXiv:1412.3555

    arXiv 2014

  7. [7]

    Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering . In Advances in Neural Information Processing Systems , 3844--3852

    work page 2016

  8. [8]

    K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R

    Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints . In Advances in Neural Information Processing Systems , 2224--2232

    work page 2015

Show all 56 references
  1. [9]

    S.; Riley, P

    Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017. Neural Message Passing for Quantum Chemistry . Proceedings of the 34th International Conference on Machine Learning 1263--1272

    2017

  2. [10]

    Gori, M.; Monfardini, G.; and Scarselli, F. 2005. A New Model for Learning in Graph Domains . In Proceedings of the 2005 IEEE International Joint Conference on Neural Networks , volume 2, 729--734

    2005

  3. [11]

    Greene, D., and Cunningham, P. 2006. Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering . In Proceedings of the 23rd International Conference on Machine Learning , 377--384

    2006

  4. [12]

    Hamilton, W.; Ying, Z.; and Leskovec, J. 2017. Inductive Representation Learning on Large Graphs . In Advances in Neural Information Processing Systems , 1024--1034

    2017

  5. [13]

    He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016. Deep Residual Learning for Image Recognition . In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 770--778

    2016

  6. [14]

    Henaff, M.; Bruna, J.; and LeCun, Y. 2015. Deep Convolutional Networks on Graph-Structured Data . arXiv preprint arXiv:1506.05163

    2015 arXiv

  7. [15]

    Irsoy, O., and Cardie, C. 2014. Deep Recursive Neural Networksfor Compositionality in Language . In Advances in Neural Information Processing Systems , 2096--2104

    2014

  8. [16]

    Iyyer, M.; Manjunatha, V.; Boyd-Graber, J.; and Daum \'e III, H. 2015. Deep Unordered Composition Rivals Syntactic Methods for Text Classification . In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confe...

    2015

  9. [17]

    Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; and Riley, P. 2016. Molecular graph convolutions: moving beyond fingerprints. Journal of Computer-Aided Molecular Design 30(8):595--608

    2016

  10. [18]

    Kim, Y. 2014. Convolutional Neural Networks for Sentence Classification . In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing , 1746--1751

    2014

  11. [19]

    P., and Ba, J

    Kingma, D. P., and Ba, J. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

    2014 arXiv

  12. [20]

    N., and Welling, M

    Kipf, T. N., and Welling, M. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

    2016 arXiv

  13. [21]

    Kusner, M.; Sun, Y.; Kolkin, N.; and Weinberger, K. 2015. From Word Embeddings to Document Distances . In Proceedings of the 32nd International Conference on Machine Learning , 957--966

    2015

  14. [22]

    Le, Q., and Mikolov, T. 2014. Distributed Representations of Sentences and Documents . In Proceedings of the 31st International Conference on Machine Learning , 1188--1196

    2014

  15. [23]

    Li, X., and Roth, D. 2002. Learning Question Classifiers . In Proceedings of the 19th International Conference on Computational Linguistics , 1--7

    2002

  16. [24]

    Li, Y.; Tarlow, D.; Brockschmidt, M.; and Zemel, R. 2016. Gated graph sequence neural networks. In Proceedings of the 4th International Conference on Learning Representations

    2016

  17. [25]

    Li, J.; Luong, T.; and Jurafsky, D. 2015. A Hierarchical Neural Autoencoder for Paragraphs and Documents . In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing , 1106--1115

    2015

  18. [26]

    Lin, R.; Liu, S.; Yang, M.; Li, M.; Zhou, M.; and Li, S. 2015. Hierarchical Recurrent Neural Network for Document Modeling . In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing , 899--907

    2015

  19. [27]

    Lin, Z.; Feng, M.; Santos, C. N. d.; Yu, M.; Xiang, B.; Zhou, B.; and Bengio, Y. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130

    2017 arXiv

  20. [28]

    L.; Daly, R

    Maas, A. L.; Daly, R. E.; Pham, P. T.; Huang, D.; Ng, A. Y.; and Potts, C. 2011. Learning Word Vectors for Sentiment Analysis . In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , 142--150

    2011

  21. [29]

    Mihalcea, R., and Tarau, P. 2004. TextRank: Bringing Order into Texts . In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing , 404--411

    2004

  22. [30]

    S.; and Dean, J

    Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013. Distributed Representations of Words and Phrases and their Compositionality . In Advances in Neural Information Processing Systems , 3111--3119

    2013

  23. [31]

    P.; Weiss, Y.; and Jordan, M

    Murphy, K. P.; Weiss, Y.; and Jordan, M. I. 1999. Loopy Belief Propagation for Approximate Inference: An Empirical Study . In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence , 467--475

    1999

  24. [32]

    Niepert, M.; Ahmed, M.; and Kutzkov, K. 2016. Learning Convolutional Neural Networks for Graphs . In Proceedings of the 33rd International Conference on Machine Learning , 2014--2023

    2016

  25. [33]

    Nikolentzos, G.; Meladianos, P.; Rousseau, F.; Stavrakas, Y.; and Vazirgiannis, M. 2017. Shortest-Path Graph Kernels for Document Similarity . In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 1890--1900

    2017

  26. [34]

    J.-P.; Skianis, K.; and Vazirgiannis, M

    Nikolentzos, G.; Meladianos, P.; Tixier, A. J.-P.; Skianis, K.; and Vazirgiannis, M. 2018. Kernel Graph Convolutional Neural Networks . In Proceedings of the 27th International Conference on Artificial Neural Networks , 22--32

    2018

  27. [35]

    Pang, B., and Lee, L. 2004. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts . In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics , 271--278

    2004

  28. [36]

    Pang, B., and Lee, L. 2005. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales . In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , 115--124

    2005

  29. [37]

    Peng, H.; Li, J.; He, Y.; Liu, Y.; Bao, M.; Wang, L.; Song, Y.; and Yang, Q. 2018. Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN . In Proceedings of the 2018 World Wide Web Conference , 1063--1072

    2018

  30. [38]

    Raheja, V., and Tetreault, J. 2019. Dialogue Act Classification with Context-Aware Self-Attention . arXiv preprint arXiv:1904.02594

    2019 arXiv

  31. [39]

    R eh u r ek, R., and Sojka, P. 2010. Software Framework for Topic Modelling with Large Corpora . In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks , 45--50

    2010

  32. [40]

    C.; Hagenbuchner, M.; and Monfardini, G

    Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008. The Graph Neural Network Model . IEEE Transactions on Neural Networks 20(1):61--80

    2008

  33. [41]

    J.-P.; Vazirgiannis, M.; and Lorr \'e , J.-P

    Shang, G.; Tixier, A. J.-P.; Vazirgiannis, M.; and Lorr \'e , J.-P. 2019. Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding . arXiv preprint arXiv:1904.09491

    2019 arXiv

  34. [42]

    Shen, T.; Zhou, T.; Long, G.; Jiang, J.; Pan, S.; and Zhang, C. 2018. DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence , 5446--5455

    2018

  35. [43]

    D.; Ng, A.; and Potts, C

    Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A.; and Potts, C. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank . In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , 1631--1642

    2013

  36. [44]

    Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting . The Journal of Machine Learning Research 15(1):1929--1958

    2014

  37. [45]

    S.; Socher, R.; and Manning, C

    Tai, K. S.; Socher, R.; and Manning, C. D. 2015. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks . In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Na...

    2015

  38. [46]

    Tang, D.; Qin, B.; and Liu, T. 2015. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification . In Proceedings of the 2015 conference on Empirical Methods in Natural Language Processing , 1422--1432

    2015

  39. [47]

    J.-P.; Nikolentzos, G.; Meladianos, P.; and Vazirgiannis, M

    Tixier, A. J.-P.; Nikolentzos, G.; Meladianos, P.; and Vazirgiannis, M. 2019. Graph Classification with 2D Convolutional Neural Networks . In Proceedings of the 28th International Conference on Artificial Neural Networks , 578--593

    2019

  40. [48]

    N.; Kaiser, .; and Polosukhin, I

    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, .; and Polosukhin, I. 2017. Attention Is All You Need . In Advances in Neural Information Processing Systems , 5998--6008

    2017

  41. [49]

    Veli c kovi \'c , P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903

    2017 arXiv

  42. [50]

    Weisfeiler, B., and Lehman, A. A. 1968. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia 2(9):12--16

    1968

  43. [51]

    Wiebe, J.; Wilson, T.; and Cardie, C. 2005. Annotating Expressions of Opinions and Emotions in Language . Language Resources and Evaluation 39(2-3):165--210

    2005

  44. [52]

    Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018a. How Powerful are Graph Neural Networks? arXiv preprint arXiv:1810.00826

    arXiv

  45. [53]

    Xu, K.; Li, C.; Tian, Y.; Sonobe, T.; Kawarabayashi, K.-i.; and Jegelka, S. 2018b. Representation Learning on Graphs with Jumping Knowledge Networks . In Proceedings of the 35th International Conference on Machine Learning , 5449--5458

  46. [54]

    Yang, Z.; Yang, D.; Dyer, C.; He, X.; Smola, A.; and Hovy, E. 2016. Hierarchical Attention Networks for Document Classification . In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ,...

    2016

  47. [55]

    Yao, L.; Mao, C.; and Luo, Y. 2019. Graph Convolutional Networks for Text Classification . In Proceedings of the 33rd AAAI Conference on Artificial Intelligence , 7370--7377

    2019

  48. [56]

    Zhou, C.; Sun, C.; Liu, Z.; and Lau, F. 2015. A C-LSTM Neural Network for Text Classification . arXiv preprint arXiv:1511.08630

    2015 arXiv

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.