Pith. sign in

REVIEW 3 major objections 5 minor 37 references

Syntax-aware Multilingual Semantic Role Labeling

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 A syntax-guided pruning rule plus BERT reaches new state-of-the-art SRL scores on all seven CoNLL-2009 benchmark languages.

desk verdict A solid multilingual SRL paper that delivers real SOTA numbers for previously stagnant languages, though the pruning threshold k is selected with test-set F1 in sight and the AP-only gains are not significance-tested. read the letter →

arxiv 1909.00310 v3 pith:NJSZYVDL submitted 2019-09-01 cs.CL

Shexia He , Zuchao Li , Hai Zhao This is my paper · ORCID
classification cs.CL
keywords multilingualsemanticrolelabelingargumentpruningsyntacticdependencytreedistancetuplebiaffineattentionBERTELMoCoNLL-2009
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 tries to establish that multilingual semantic role labeling—assigning predicate–argument roles in many languages at once—can be improved uniformly by a simple syntactic pruning rule rather than by language-specific feature engineering. The rule keeps only predicate–argument pairs whose dependency-tree distance tuple is among the most frequent seen in training data, and applies it inside the network so the sentence encoding stays intact. With that layer plus fixed BERT embeddings, the single unified model reports new state-of-the-art semantic F1 scores on all seven CoNLL-2009 languages. The result matters because Catalan and Japanese had not been improved since the 2009 shared task, and because the method suggests syntax can be injected cheaply across typologically different languages.

What carries the argument

The central object is the distance tuple $(d_p, d_a)$ on a syntactic dependency tree: $d_p$ and $d_a$ are the distances from the predicate and the argument, respectively, up to their nearest common ancestor, so $(0,1)$ means the argument is a child of the predicate. The paper counts these tuples per language in the training data, ranks them by frequency, and takes the top-$k$ (top-20 for most languages, top-120 for Japanese) as the syntactic rule. The argument-pruning layer then drops the BiLSTM hidden states of any candidate argument whose tuple is not in that list, before the biaffine scorer sees it. This carries the argument by cutting the large majority of non-argument candidates while preserving over 99% coverage of true arguments, and it does so without breaking the integrity of the whole-sentence representation that earlier $k$-order pruning was criticized for.

What would settle it

Take the trained per-language top-$k$ tuple lists and count, on an out-of-domain test set (for example news-trained models evaluated on fiction or conversation), the fraction of gold arguments whose distance tuple falls outside the list. If that coverage drops noticeably below the reported roughly 99% on in-domain data, recall on that domain will fall and the method's gains will not transfer; the same audit can be run on any new language before trusting the pruning rule.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that its unified SRL model—a BiLSTM encoder, an argument-pruning layer guided by a per-language syntactic rule, and a biaffine scorer—achieves semantic F1 of 85.14 on Catalan, 86.42 on Chinese, 89.66 on Czech, 90.86 on English, 80.87 on German, 83.76 on Japanese, and 84.60 on Spanish on the in-domain CoNLL-2009 test sets, each exceeding the previously published best result. The pruning contribution is shown separately: adding the argument-pruning layer to the syntax-agnostic baseline improves F1 on every language, and using gold rather than predicted syntax improves it further, especially for Catalan and Spanish where all gold arguments sit directly under the predicate. The paper also finds that BERT embeddings outperform ELMo on all seven languages, and that the gains from both pruning and contextualized embeddings are smallest for Japanese, which has the smallest training set.

Load-bearing premise

The load-bearing premise is that the top-$k$ distance tuples counted from training data will cover almost all true predicate–argument pairs in test data; if a new genre or a language with freer word order produces argument positions outside that set, the pruning layer discards correct arguments before the scorer can label them.

Editorial extensions

If this is right

  • The model sets a new state of the art on all seven CoNLL-2009 in-domain test sets, giving Catalan and Japanese their first improvement since the 2009 shared task.
  • Argument pruning consistently beats the strong syntax-agnostic baseline on every language, with F1 gains from roughly 0.04 to 0.50 points.
  • Switching from predicted to gold syntax raises scores further, up to +1.43 F1 on Catalan, so parse quality directly bounds the benefit of the syntactic rule.
  • BERT outweighs ELMo in every language tested, indicating that deeper contextualized representations are the main driver of the top-line results.

Reading between the lines

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

  • Because the pruning rule is a per-language frequency ranking, its rankings could be transferred from a high-resource to a low-resource language; whether coverage of true arguments survives that transfer is an open, testable question.
  • Since the pruning operates on hidden states rather than tokens, the layer could be inserted into other BiLSTM-plus-scorer architectures as a drop-in module, potentially reducing training time without retraining the encoder.
  • The recurrence of $(0,1)$ as the most frequent tuple across languages hints at a universal tendency for arguments to be children of their predicate; a cross-linguistic check on free-word-order languages would show whether the top-$k$ rule is genuinely language-neutral or biased toward fixed-order languages.
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 a multilingual dependency-based semantic role labeling model built on a biaffine scorer, augmented with a syntax-guided argument pruning layer. The pruning rule is derived from language-specific counts of (dp, da) distance tuples between predicates and arguments in dependency parse trees, keeping only the top-k most frequent tuples. The model is evaluated on all seven CoNLL-2009 languages, with and without frozen ELMo/BERT embeddings, and the authors report new state-of-the-art semantic F1 scores on all seven benchmarks. The paper also includes ablations of POS/lemma features, a comparison with the k-order pruning method of He et al. (2018), and an analysis of predicted versus gold syntax.

Significance. If the empirical claims hold up, this would be a useful contribution: it provides the first comprehensive deep-learning update for multilingual SRL on all CoNLL-2009 languages since the shared task, and it integrates syntax through a simple, uniform pruning mechanism rather than language-specific architectural changes. The authors release code, which is valuable for reproducibility. The analysis of gold versus predicted syntax and the per-language observations about Japanese are also informative. However, the central novelty of the paper is the argument pruning method, and its reported gains over the syntax-agnostic baseline are small; the strength of the SOTA claim therefore depends critically on the experimental protocol, especially how the pruning threshold k is chosen.

major comments (3)
  1. [Section 3 and Section 4.2, Figure 3] The pruning threshold k is selected using test-set performance. Section 3 states that top-120 distance tuples are used for Japanese and top-20 for other languages, and Section 4.2/Figure 3 plots test-set F1 as a function of k, reporting that "k = 20 yields the best performance" and that "top-120 can achieve the best results" for Japanese. This means the reported +AP and +AP+BERT scores are maxima over k on the test labels rather than evaluations of a fixed model. Because the AP-only gains in Tables 2 and 3 are only 0.2-0.5 F1, the pruning contribution may be partly or entirely an artifact of test-set selection. Please re-select k on a development split (or by cross-validation) and report the corresponding test results, and discuss how much the reported numbers change.
  2. [Section 2.2] The method's premise is that true predicate-argument pairs almost always fall within the top-k distance tuples, but the paper never reports the oracle coverage of the pruning rule, i.e., the fraction of gold arguments retained by the top-k filter. Without this information, the reader cannot tell whether the pruning layer is dropping true arguments and whether the F1 gains come from better candidate selection or from a precision/recall trade-off. Please report, for each language, the oracle coverage and candidate recall of the pruning layer on the training and test splits, for both predicted and gold syntax.
  3. [Section 3, Model Setup; Tables 2 and 3] All reported results are single-run point estimates with no variance or significance testing. The AP-only improvements are small (0.10-0.50 F1 on most languages), and the comparison with k-order pruning in Table 6 involves differences of similar magnitude. Given the test-set-based threshold selection, the evidence for the central claim would be substantially stronger with multiple random seeds, standard deviations, or a paired significance test across runs.
minor comments (5)
  1. [Figure 3] The caption should state explicitly that the curves are test-set F1 values and that k=0 corresponds to the no-pruning baseline; the current text leaves this to the body.
  2. [Section 4.2] The sentence "the total number of distance tuples in syntactic rule is no more than 120 in these languages except that Japanese is about 260" is inconsistent with the earlier statement that top-120 is used for Japanese; please clarify the relationship between the total number of observed tuples and the chosen threshold.
  3. [Abstract and Introduction] There are several grammatical issues, e.g., "has earned a series of success" and "so that is kept underdevelopment"; the paper would benefit from a careful language edit.
  4. [Section 3.1] The description of BERT as "BERT-Base, Multilingual Cased" is fine, but it would help to state explicitly whether the contextual embeddings are extracted from a specific layer or averaged, since this is known to affect downstream performance.
  5. [Section 4.3] The authors honestly note that syntactic contribution is overall limited and leave more effective syntax integration to future work; this is a useful caveat, but it also underscores the need for the statistical validation requested above.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported pruning gains are partially constructed by choosing the top-k threshold on the test set; the SOTA claim otherwise rests on direct benchmark evaluation.

  1. fitted input called prediction [Section 3 (model setup) and Section 4.2/Figure 3]
    "Figure 3 shows F 1 scores on test set by top-k argument pruning for German, Catalan and Japanese... We observe that the case of k = 20 yields the best performance on German and Catalan. As for Japanese, it falls short of the baseline in our current observation range, but our experiment has shown that the setting of top-120 can achieve the best results. For syntactic rule in argument pruning layer... we use the top-120 distance tuples on Japanese and top-20 on other multiple languages."

    The top-k value is the pruning rule's only hyperparameter. The paper sets k=20 (most languages) and k=120 (Japanese) after inspecting the test-set F1 curve, i.e. k is chosen to maximize the exact metric reported in Tables 2-3. The +AP and +AP+BERT results are therefore not evaluations of a fixed model but maxima over a k grid on the target test labels. Since the paper's central validation of the novel pruning method is the +AP gain over the syntax-agnostic baseline (0.35-0.5 F1), that gain is partially constructed by test-set selection rather than an independent prediction. This is a fitted parameter being presented as a principled 'syntactic rule'.

full rationale

The paper is otherwise an empirical CoNLL-2009 benchmark study: the baseline, biaffine scorer, and comparison systems are standard, and the reported scores are direct evaluations on held-out test sets. Self-citations (Cai et al. 2018, He et al. 2018, Li et al. 2018/2019) are used as baselines and prior work, not as load-bearing proofs; no uniqueness theorem is invoked. However, the one clear circularity is the test-set selection of the pruning threshold k. Figure 3 explicitly plots test F1 as a function of k, and the chosen k values (20, 120) are justified by 'best performance' on that test set. Thus the AP-related results—the paper's claimed novel contribution—are not clean held-out predictions. The BERT-based SOTA scores would likely stand without the pruning contribution, so the overall SOTA claim has independent content, but the pruning effectiveness claim is partially fitted. Score 6 reflects a partial reduction: one central claimed improvement is selected on the test set rather than derived or validated on a held-out split.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central empirical result rests on standard supervised benchmark assumptions plus one language-specific hyperparameter (top-k) and the domain assumption that parse-tree distance tuples localize arguments. No new physical or theoretical entities are introduced.

free parameters (1)
  • top-k distance tuple threshold = 20 for six languages, 120 for Japanese
    The number of frequent predicate-argument distance tuples kept in the pruning rule is chosen per language. The text says top-120 is used for Japanese and top-20 for others to keep more than 99% coverage, while Figure 3 shows test-set F1 varying with k, implying test-set tuning.
assumptions (3)
  • domain assumption Dependency-tree distance tuples are a sufficient proxy for argument locality across all seven languages.
    The entire pruning layer in Section 2.2 relies on this. If true arguments are frequently far from predicates in the parse tree, pruning would remove them and hurt recall.
  • domain assumption The official CoNLL-2009 predicted syntactic parses are accurate enough to support the pruning rule in all languages.
    The experiments use officially provided predicted syntax, with gold syntax used only in the analysis section. Noisy parses could break the distance-tuple statistics.
  • domain assumption Fixed ELMo and multilingual BERT embeddings provide useful semantic representations for all seven languages without fine-tuning.
    The SOTA results in Table 3 depend on concatenating these pretrained contextual embeddings; if the multilingual embeddings were weak for some languages, the reported gains would not transfer.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Syntax-aware Multilingual Semantic Role Labeling." pith.science (2026). https://pith.science/paper/NJSZYVDL

@misc{pith2026190900310,
  author       = {Pith},
  title        = {Pith review of: Syntax-aware Multilingual Semantic Role Labeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NJSZYVDL}},
  note         = {Machine review of arXiv:1909.00310}
}
read the original abstract

Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment. Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation. Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively. We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement. Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.

Figures

Figures reproduced from arXiv: 1909.00310 by the authors.

Figure 1
Figure 1. Overall architecture of our SRL model. Red denotes the given predicate, and gray indicates that these [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Syntactic parse tree examples (dependency [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. F1 scores on test set by top-k argument prun￾ing for German, Catalan and Japanese. 4 Analysis In this section, we perform further analysis to bet￾ter understand our model, exploring the impact of language features, syntactic rule and syntac￾tic contribution for multilingual SRL. Since recent work well studied dependency SRL on English and Chinese, we focus on other five languages, and these analyses are performed on… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 34 canonical work pages

  1. [1]

    Anders Bj\" o rkelund, Love Hafdell, and Pierre Nugues. 2009. Multilingual semantic role labeling. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task, pages 43--48

    work page 2009

  2. [2]

    Jiaxun Cai, Shexia He, Zuchao Li, and Hai Zhao. 2018. A full end-to-end semantic role labeler, syntactic-agnostic over syntactic-aware? In Proceedings of the 27th International Conference on Computational Linguistics (COLING), pages 2753--2765

    work page 2018

  3. [3]

    Wanxiang Che, Yijia Liu, Yuxuan Wang, Bo Zheng, and Ting Liu. 2018. Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies , pages 55--64

    work page 2018

  4. [4]

    Janara Christensen, Mausam, Stephen Soderland, and Oren Etzioni. 2011. An analysis of open information extraction based on semantic role labeling. In Proceedings of the 6th International Conference on Knowledge Capture, pages 113--120

    work page 2011

  5. [5]

    Ronan Collobert, Jason Weston, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(1):2493--2537

    work page 2011

  6. [6]

    Jacob Devlin, Ming - Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

    arXiv 2018

  7. [7]

    Timothy Dozat and Christopher D Manning. 2017. Deep biaffine attention for neural dependency parsing. In Proceedings of 5th International Conference on Learning Representations (ICLR)

    work page 2017

  8. [8]

    a ckstr \

    Nicholas FitzGerald, Oscar T \"a ckstr \"o m, Kuzman Ganchev, and Dipanjan Das. 2015. Semantic role labeling with neural network factors. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 960--970

    work page 2015

Show all 37 references
  1. [9]

    William Foland and James Martin. 2015. Dependency-based semantic role labeling using convolutional neural networks. In Joint Conference on Lexical and Computational Semantics, pages 279--288

    2015

  2. [10]

    Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018. Learning word vectors for 157 languages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC)

    2018

  3. [11]

    Jan Haji c , Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Ant\` o nia Mart\' , Llu\' i s M\` a rquez, Adam Meyers, Joakim Nivre, Sebastian Pad\' o , Jan S t e p\' a nek, Pavel Stra n \' a k, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. 2009. The CoNLL -2009...

    2009

  4. [12]

    Shexia He, Zuchao Li, Hai Zhao, Hongxiao Bai, and Gongshen Liu. 2018. Syntax for semantic role labeling, to be, or not to be. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 2061--2071

    2018

  5. [13]

    Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735--1780

    1997

  6. [14]

    Jungo Kasai, Dan Friedman, Robert Frank, Dragomir Radev, and Owen Rambow. 2019. Syntax-aware neural semantic role labeling with supertags. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techn...

    2019

  7. [15]

    Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR)

    2015

  8. [16]

    Tao Lei, Yuan Zhang, Llu\' i s M\` a rquez, Alessandro Moschitti, and Regina Barzilay. 2015. High-order low-rank tensors for semantic role labeling. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lang...

    2015

  9. [17]

    Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, and Luo Si. 2018. A unified syntax-aware framework for semantic role labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2401--2411

    2018

  10. [18]

    Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, and Xiang Zhou. 2019. Dependency or span, end-to-end uniform semantic role labeling. arXiv preprint arXiv:1901.05280

    2019 arXiv

  11. [19]

    Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017. A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 411--420

    2017

  12. [20]

    Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1506--1515

    2017

  13. [21]

    Phoebe Mulcaire, Swabha Swayamdipta, and Noah A. Smith. 2018. Polyglot semantic role labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 667--672

    2018

  14. [22]

    Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532--1543

    2014

  15. [23]

    Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

    Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics...

    2018

  16. [24]

    Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James Martin, and Daniel Jurafsky. 2005. Semantic role labeling using different syntactic views. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 581--588

    2005

  17. [25]

    Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics, 34(2):257--287

    2008

  18. [26]

    Michael Roth and Mirella Lapata. 2016. Neural semantic role labeling with dependency path embeddings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1192--1202

    2016

  19. [27]

    Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018. Linguistically-informed self-attention for semantic role labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5027--5038

    2018

  20. [28]

    Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2016. Greedy, joint syntactic-semantic parsing with stack lstms. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL), pages 187--197

    2016

  21. [29]

    Deyi Xiong, Min Zhang, and Haizhou Li. 2012. Modeling the translation of predicate-argument structure for SMT . In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL), pages 902--911

    2012

  22. [30]

    Nianwen Xue and Martha Palmer. 2004. Calibrating features for semantic role labeling. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 88--94

    2004

  23. [31]

    Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, and Jina Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 201--206

    2016

  24. [32]

    Zhisong Zhang, Hai Zhao, and Lianhui Qin. 2016. Probabilistic graph-based dependency parsing with convolutional neural network. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1382--1392

    2016

  25. [33]

    Hai Zhao, Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto, and Kentaro Torisawa. 2009 a . Multilingual dependency learning: Exploiting rich features for tagging syntactic and semantic dependencies. In Proceedings of the Thirteenth Conference on Computational Natural Language...

    2009

  26. [34]

    Hai Zhao, Wenliang Chen, and Chunyu Kit. 2009 b . Semantic dependency parsing of NomBank and PropBank : An efficient integrated approach via a large-scale feature selection. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 30--39

    2009

  27. [35]

    Hai Zhao, Xiaotian Zhang, and Chunyu Kit. 2013. Integrative semantic dependency parsing via efficient large-scale feature selection. Journal of Artificial Intelligence Research, 46:203--233

    2013

  28. [36]

    Jie Zhou and Wei Xu. 2015. End-to-end learning of semantic role labeling using recurrent neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 1127--1137, Beijing, China

    2015

  29. [37]

    Junru Zhou and Hai Zhao. 2019. Head-driven phrase structure grammar parsing on P enn treebank. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)

    2019

Pith tools

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