Task Decomposition for Efficient Annotation

Emma Strubell; Nupoor Gandhi

REVIEW 2 major objections 1 minor 299 references

Task Decomposition for Efficient Annotation

T0 review · 2 major / 1 minor · reviewed 2026-06-25 · grok-4.3

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

Pith's one-line read Decomposing structured annotation tasks by isolating center identification reduces aggregate inferential load.

desk verdict The decomposition guidelines and allocation procedure are the practical bits worth a look, but the formal model of inferential load has no shown link to actual annotator effort. read the letter →

arxiv 2606.24734 v1 pith:2GD2H5VV submitted 2026-06-23 cs.CL cs.AIcs.HC

Nupoor Gandhi , Emma Strubell This is my paper · ORCID

classification cs.CLcs.AIcs.HC

keywords taskdecompositionannotationefficiencyinferentialloadcenteringtheorystructuredhuman-AIcollaborationcost-efficiency

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 proposes breaking complex structured annotation into sub-tasks to lower the total difficulty faced by mixed teams of human and model annotators. It builds a formal model that treats inferential load as the degrees of freedom left in the space of valid annotations. Using this model, the authors show that first locating centers—salient anchor entities—narrows the possible outputs and thereby cuts the overall load. The work supplies concrete guidelines for performing such decompositions and a procedure for assigning the resulting sub-tasks to different annotators so that quality is maximized within a fixed budget.

What carries the argument

Formal model of inferential load defined by degrees of freedom in the space of valid annotations, with centers from centering theory serving as the salient anchor entities that sub-tasks must realize.

What would settle it

A controlled experiment on the same corpus that measures total annotation time or downstream error rate for end-to-end versus center-first decomposed workflows and finds no reduction in load for the decomposed version.

Watch

Extended reading notes

Core claim

By modeling inferential load through degrees of freedom in the space of valid annotations, the paper establishes that annotation decompositions which isolate and advance the identification of centers—salient anchor entities realized by sub-tasks—constrain output space complexity and reduce the aggregate inferential load. This holds across heterogeneous annotators that include both models and humans with varying expertise, and it is supported by guidelines and allocation procedures illustrated with prior cost-efficiency examples.

Load-bearing premise

The formal count of degrees of freedom in valid annotations accurately captures the practical difficulty experienced by human or model annotators.

Editorial extensions

If this is right

Decompositions that isolate center identification constrain output space complexity.
Allocating sub-tasks across heterogeneous annotators maximizes quality under a fixed budget.
Guidelines for decomposition produce measurable cost-efficiency gains as shown in prior examples.
Modern annotation projects can redesign workflows to match distinct challenges to annotator strengths.

Reading between the lines

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

The same decomposition logic could be tested on other structured prediction tasks such as semantic parsing or information extraction.
Dynamic assignment of sub-tasks might further improve efficiency if annotator performance on each sub-task can be estimated in advance.
Empirical measurement of actual annotation time before and after decomposition would provide a direct test of the degrees-of-freedom model.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The main takeaway is that the paper frames annotation decomposition around centers from centering theory, with a model that treats inferential load as degrees of freedom in the valid annotation space. They argue that isolating center identification shrinks that space and lowers total load, then give guidelines plus an allocation procedure for mixed human-model teams under a fixed budget.

What stands out as new is the explicit tie to centering theory for this purpose and the attempt to turn it into an allocation rule. The guidelines are backed by examples from the authors' prior work that reportedly improved cost-efficiency, which at least gives the ideas some grounding in real projects. The allocation step for heterogeneous annotators is a direct response to a common practical headache.

The soft spot is the missing connection between the degrees-of-freedom metric and real effort. The stress-test concern holds: nothing in the abstract shows that the model predicts lower time, error rates, or cognitive load once you account for task-switching costs or interface issues. If the full paper has no experiments or correlations against observed annotator behavior, the formal model stays more definitional than predictive.

This is aimed at teams running large structured annotation projects in NLP, especially those mixing models and humans with different expertise. A reader who needs concrete ways to split tasks and assign them under budget constraints could pull useful pieces from the guidelines and procedure.

It should go to peer review. The angle is reasonable and the allocation procedure is a concrete contribution even if the load model needs more empirical support.

Referee Report

2 major / 1 minor

Summary. The paper claims that decomposing structured annotation tasks into sub-tasks reduces aggregate inferential load on heterogeneous annotators (humans and models). Drawing from centering theory, it introduces a formal model of inferential load defined via degrees of freedom in the space of valid annotations; identifying 'centers' (salient anchor entities) is argued to constrain output-space complexity, with decompositions that isolate center identification thereby lowering total load. The manuscript supplies guidelines for such decompositions, illustrates them with cost-efficiency examples drawn from the authors' prior work, and outlines a procedure for allocating sub-tasks across annotators to maximize quality under a fixed budget.

Significance. If the formal model is shown to track real annotator effort and the allocation procedure yields measurable gains, the approach could meaningfully improve efficiency and quality control for large-scale structured annotation in NLP, especially when mixing model and human annotators with differing expertise.

major comments (2)

[formal model of inferential load] The section introducing the formal model of inferential load: the definition of load as degrees of freedom in the valid-annotation space is presented without any derivation, empirical correlation, or validation against observable annotator metrics (time, error rate, cognitive load). This assumption is load-bearing for the central claim that center-isolating decompositions reduce practical difficulty.
[guidelines and examples] The section on guidelines and examples: cost-efficiency improvements are supported solely by references to prior work rather than new experiments that apply the proposed decomposition procedure and measure the predicted load reduction.

minor comments (1)

[abstract] The abstract refers to 'our prior work' without citations; adding specific references would clarify the empirical grounding of the examples.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major point below, clarifying the theoretical nature of the contribution while agreeing where revisions can strengthen the manuscript.

read point-by-point responses

Referee: [formal model of inferential load] The section introducing the formal model of inferential load: the definition of load as degrees of freedom in the valid-annotation space is presented without any derivation, empirical correlation, or validation against observable annotator metrics (time, error rate, cognitive load). This assumption is load-bearing for the central claim that center-isolating decompositions reduce practical difficulty.

Authors: The degrees-of-freedom formulation is introduced as a direct formalization of centering theory's notion of salience constraining discourse entities, rather than as an empirically fitted metric. No derivation from first principles or correlation to time/error rates is provided because the paper's focus is the resulting decomposition guidelines, not metric validation. We will revise the model section to include an explicit step-by-step derivation showing how annotation constraints map to degrees of freedom. revision: yes
Referee: [guidelines and examples] The section on guidelines and examples: cost-efficiency improvements are supported solely by references to prior work rather than new experiments that apply the proposed decomposition procedure and measure the predicted load reduction.

Authors: The guidelines are illustrated with cost-efficiency outcomes from our earlier annotation projects to show concrete applicability; the manuscript is a methodological proposal, not an empirical study. New experiments applying the procedure and measuring load would be a natural next step but fall outside the current scope. We will add a short subsection outlining how such validation experiments could be designed. revision: partial

Circularity Check

1 steps flagged · score 6.0 of 10

Formal model equates inferential load to degrees of freedom, making reduction claims tautological by definition

self definitional [Abstract]
"we introduce a formal model of inferential load based on the degrees of freedom in the space of valid annotations. Using this model, we show that identifying these centers (i.e. salient anchor entities realized by annotation sub-tasks) constrains the output space complexity, and decompositions which isolate and advance center identification reduce the aggregate inferential load."

Inferential load is defined as degrees of freedom in the valid annotation space. The result that center-identifying decompositions reduce aggregate load follows directly from the definition (constraining the space reduces degrees of freedom), without additional steps or validation against actual annotator effort.

full rationale

The paper introduces a formal model defining inferential load explicitly as degrees of freedom in the annotation space, then uses that model to 'show' that center identification reduces load by constraining the space. This reduction holds by construction of the definition itself rather than through independent derivation or external evidence. Support for guidelines also draws from the authors' prior work, adding a self-citation element, but the core formal step is self-definitional. No equations or further derivations are visible to alter this assessment.

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

Abstract-only review; limited visibility into parameters or assumptions beyond those stated in the proposal.

assumptions (1)

domain assumption Centering theory supplies a useful notion of salient anchor entities for constraining annotation spaces
Explicitly invoked as inspiration for the formal model in the abstract.

invented entities (1)

formal model of inferential load based on degrees of freedom
purpose: To quantify and compare aggregate difficulty across different task decompositions
Introduced to justify why center-focused decompositions reduce load

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Cite this review

Pith. "Pith review of Task Decomposition for Efficient Annotation." pith.science (2026). https://pith.science/paper/2GD2H5VV

@misc{pith2026260624734,
  author       = {Pith},
  title        = {Pith review of: Task Decomposition for Efficient Annotation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2GD2H5VV}},
  note         = {Machine review of arXiv:2606.24734}
}

read the original abstract

High-quality annotations of structured representations are expensive to collect over large corpora. Manual annotation of structure is laborious, and model-based annotation, although cheaper to generate, requires expensive validation and potentially significant supervision to ensure that the annotation quality is strong enough to be useful downstream. In traditional annotation workflows, annotation of each complete example is performed end-to-end by a single annotator. However, structured annotation is complex, and each aspect of the task represents a unique challenge with an associated inferential load for a given annotator. Modern annotation projects can incorporate heterogeneous groups of annotators, including both models and human annotators with varying domain and linguistic expertise. It remains unclear, however, how to redesign annotation tasks in this setting, where efforts are discriminately allocated across heterogeneous annotators with respect to distinct annotation challenges. We propose to decompose annotation tasks into sub-tasks in order to reduce the aggregate inferential load of annotation projects. Inspired by the notion of centers from centering theory, we introduce a formal model of inferential load based on the degrees of freedom in the space of valid annotations. Using this model, we show that identifying these centers (i.e. salient anchor entities realized by annotation sub-tasks) constrains the output space complexity, and decompositions which isolate and advance center identification reduce the aggregate inferential load. We provide guidelines for decomposing complex structured annotation tasks, supported by examples demonstrating improved cost-efficiency from our prior work. Finally, we present a procedure for allocating sub-tasks across annotators to maximize quality under a fixed budget.

Figures

Figures reproduced from arXiv: 2606.24734 by the authors.

**Figure 1.** We illustrate the atomic space for a coreference resolution task decomposition, where 𝑡1 is typed mention identification and 𝑡2 is antecedent linking. The top row represents the atomic space of a single instance of task 𝑡1, and the subsequent rows represent instances of the task 𝑡2. The dashed links between boxes denote the transfer of seed centers between sub-tasks. We denote the introduction of a new center in ye… view at source ↗

**Figure 2.** The top row is a pictoral representation of two candidate decompositions of NER: {𝑡1, 𝑡2, 𝑡3} and {𝑡1,2, 𝑡3}. The bottom row is a graphical representation of the decompositions. In the primitive decomposition graph on the left, 𝑡1 is position identification, 𝑡2 is unitization, and 𝑡3 is typing. Contracting the edge (𝑡1, 𝑡2) produces the graph on the right where 𝑡1,2 represents mention detection. Both graphs are vali… view at source ↗

**Figure 3.** The DAG illustrates a decomposed NER task, and the following table contrasts the dimensions of inferential load for the sub-tasks in the decomposition and the complete task. Taking the aggregate output space, we have (𝑇 +1) 𝐶 + 𝑀 · [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: The DAG illustrates a decomposed coreference task, and the following table contrasts the dimensions of inferential load for the sub-tasks in the decomposition and the complete task. Let the function noref(𝑖, 𝑗) = I(𝑚 𝑗 does not refer to entity(𝑚𝑖)), where the product i…

**Figure 5.** Figure 5: The DAG illustrates an excessively decomposed NER task, and the following table contrasts the dimensions of inferential load for the sub-tasks in the decomposition and the complete task. all possible spans. Let the context window from which we unitize typed positions…

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May I Ask Who ' s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance

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Noisy Text Data: Achilles ' Heel of BERT

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Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

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Combining BERT with Static Word Embeddings for Categorizing Social Media

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Enhanced Sentence Alignment Network for Efficient Short Text Matching

Hu, Zhe and Fu, Zuohui and Peng, Cheng and Wang, Weiwei. Enhanced Sentence Alignment Network for Efficient Short Text Matching. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.6

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PHINC : A Parallel H inglish Social Media Code-Mixed Corpus for Machine Translation

Srivastava, Vivek and Singh, Mayank. PHINC : A Parallel H inglish Social Media Code-Mixed Corpus for Machine Translation. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.7

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Cross-lingual sentiment classification in low-resource B engali language

Sazzed, Salim. Cross-lingual sentiment classification in low-resource B engali language. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.8

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The Non-native Speaker Aspect: I ndian E nglish in Social Media

Sarkar, Rupak and Mahinder, Sayantan and KhudaBukhsh, Ashiqur. The Non-native Speaker Aspect: I ndian E nglish in Social Media. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.9

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Sentence Boundary Detection on Line Breaks in J apanese

Hayashibe, Yuta and Mitsuzawa, Kensuke. Sentence Boundary Detection on Line Breaks in J apanese. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.10

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Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach

Yamaguchi, Yasuhiro and Inuzuka, Shintaro and Hiramatsu, Makoto and Harashima, Jun. Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.11

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Generating Fact Checking Summaries for Web Claims

Mishra, Rahul and Gupta, Dhruv and Leippold, Markus. Generating Fact Checking Summaries for Web Claims. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.12

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Intelligent Analyses on Storytelling for Impact Measurement

Kicken, Koen and De Maesschalck, Tessa and Vanrumste, Bart and De Keyser, Tom and Shim, Hee Reen. Intelligent Analyses on Storytelling for Impact Measurement. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.13

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An Empirical Analysis of Human-Bot Interaction on R eddit

Ma, Ming-Cheng and Lalor, John P. An Empirical Analysis of Human-Bot Interaction on R eddit. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.14

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Detecting Trending Terms in Cybersecurity Forum Discussions

Hughes, Jack and Aycock, Seth and Caines, Andrew and Buttery, Paula and Hutchings, Alice. Detecting Trending Terms in Cybersecurity Forum Discussions. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.15

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Service registration chatbot: collecting and comparing dialogues from AMT workers and service ' s users

Molteni, Luca and Singh, Mittul and Leinonen, Juho and Leino, Katri and Kurimo, Mikko and Della Valle, Emanuele. Service registration chatbot: collecting and comparing dialogues from AMT workers and service ' s users. Proceedings of the Sixth Workshop on Noisy User-generated T...

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Automated Assessment of Noisy Crowdsourced Free-text Answers for H indi in Low Resource Setting

Agarwal, Dolly and Gupta, Somya and Baghel, Nishant. Automated Assessment of Noisy Crowdsourced Free-text Answers for H indi in Low Resource Setting. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.17

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Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

Alam, Tanvirul and Khan, Akib and Alam, Firoj. Punctuation Restoration using Transformer Models for High-and Low-Resource Languages. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.18

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Truecasing G erman user-generated conversational text

Grishina, Yulia and Gueudre, Thomas and Winkler, Ralf. Truecasing G erman user-generated conversational text. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.19

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Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations

Alam, Md Mahfuz Ibn and Anastasopoulos, Antonios. Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.20

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Impact of ASR on A lzheimer ' s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others

Balagopalan, Aparna and Shkaruta, Ksenia and Novikova, Jekaterina. Impact of ASR on A lzheimer ' s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653...

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Detecting Entailment in Code-Mixed H indi- E nglish Conversations

Chakravarthy, Sharanya and Umapathy, Anjana and Black, Alan W. Detecting Entailment in Code-Mixed H indi- E nglish Conversations. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.22

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Detecting Objectifying Language in Online Professor Reviews

Waller, Angie and Gorman, Kyle. Detecting Objectifying Language in Online Professor Reviews. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.23

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Annotation Efficient Language Identification from Weak Labels

Palakodety, Shriphani and KhudaBukhsh, Ashiqur. Annotation Efficient Language Identification from Weak Labels. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.24

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Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach

Eyre, Ben and Balagopalan, Aparna and Novikova, Jekaterina. Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18...

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Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation

Kashefi, Omid and Hwa, Rebecca. Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.26

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An Empirical Survey of Unsupervised Text Representation Methods on T witter Data

Wang, Lili and Gao, Chongyang and Wei, Jason and Ma, Weicheng and Liu, Ruibo and Vosoughi, Soroush. An Empirical Survey of Unsupervised Text Representation Methods on T witter Data. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653...

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Sech, Justin and DeLucia, Alexandra and Buczak, Anna L. and Dredze, Mark. Civil Unrest on T witter ( CUT ): A Dataset of Tweets to Support Research on Civil Unrest. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.28

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Tweeki: Linking Named Entities on T witter to a Knowledge Graph

Harandizadeh, Bahareh and Singh, Sameer. Tweeki: Linking Named Entities on T witter to a Knowledge Graph. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.29

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Representation learning of writing style

Hay, Julien and Doan, Bich-Lien and Popineau, Fabrice and Ait Elhara, Ouassim. Representation learning of writing style. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. doi:10.18653/v1/2020.wnut-1.30

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Pith tools

Reviewed June 25, 2026 · model on record in the stance chip above.

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