Identifying Semantic Edit Intentions from Revisions in Wikipedia

Yang, Diyi; Halfaker, Aaron; Kraut, Robert E.; Hovy, Eduard

doi:10.18653/v1/d17-1213

Cited by 52 publications

(62 citation statements)

References 21 publications

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“…Few prior works have empirically explored the nature of these emergent roles. Empirical studies in the area have profiled contributors' activities, and then clustered them to identify the “bundles” of activities that represent their prototypical activity patterns (Arazy et al, , ; Liu & Ram, ; Welser et al, ; Yang, Halfaker, Kraut, & Hovy, ).…”

Section: Related Workmentioning

confidence: 99%

Neither a Bazaar nor a cathedral: The interplay between structure and agency in Wikipedia's role system

Arazy

Lifshitz-Assaf

Balila

2018

Asso for Info Science & Tech

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Roles provide a key coordination mechanism in peer‐production. Whereas one stream in the literature has focused on the structural responsibilities associated with roles, another has stressed the emergent nature of work. To date, these streams have proceeded largely in parallel. In seeking to enhance our understanding of the tension between structure and agency in peer‐production, we investigated the interplay between structural and emergent roles. Our study explored the breadth of structural roles in Wikipedia (English version) and their linkage to various forms of activities. Our analyses show that despite the latitude in selecting their mode of participation, participants' structural and emergent roles are tightly coupled. Our discussion highlights that: (a) participants often stay close to the “production ground floor” despite the assignment into structural roles; and (b) there are typical modifications in activity patterns associated with role‐assignment, namely: functional specialization, multispecialization, defunctionalization, changes in communication patterns, management of identity, and role definition. We contribute to theory of coordination and roles, as well as provide some practical implications.

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Section: Related Workmentioning

confidence: 99%

Neither a Bazaar nor a cathedral: The interplay between structure and agency in Wikipedia's role system

Arazy

Lifshitz-Assaf

Balila

2018

Asso for Info Science & Tech

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“…Wikipedia maintains snapshots of entire documents at different timestamps, which makes it possible to reconstruct edit histories for documents. This has been exploited for many NLP tasks, including sentence compression (Yamangil and Nelken, 2008), text simplification (Yatskar et al, 2010;Woodsend and Lapata, 2011;Tonelli et al, 2016) and modeling semantic edit intentions (Yang et al, 2017).…”

Section: Mining Wikipedia Editsmentioning

confidence: 99%

Learning To Split and Rephrase From Wikipedia Edit History

Botha¹,

Faruqui²,

Alex³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia's edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al.(2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.

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“…However, they use comments as meta-data to identify trusted revisions, rather than directly modeling the relationship between comments and edits. Yang et al (2017) featurize both comments and revisions to classify edit intent, but without explicitly modeling edit-comment relationship.…”

Section: Related Workmentioning

confidence: 99%

Modeling the Relationship between User Comments and Edits in Document Revision

Zhang

Rajagopal

Gamon

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Management of collaborative documents can be difficult, given the profusion of edits and comments that multiple authors make during a document's evolution. Reliably modeling the relationship between edits and comments is a crucial step towards helping the user keep track of a document in flux. A number of authoring tasks, such as categorizing and summarizing edits, detecting completed to-dos, and visually rearranging comments could benefit from such a contribution. Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring. We begin by collecting a dataset with more than half a million comment-edit pairs based on Wikipedia revision histories. We then propose a hierarchical multi-layer deep neuralnetwork to model the relationship between edits and comments. Our architecture tackles both Comment Ranking and Edit Anchoring tasks by encoding specific edit actions such as additions and deletions, while also accounting for document context. In a number of evaluation settings, our experimental results show that our approach outperforms several strong baselines significantly. We are able to achieve a precision@1 of 71.0% and a precision@3 of 94.4% for Comment Ranking, while we achieve 74.4% accuracy on Edit Anchoring.

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Identifying Semantic Edit Intentions from Revisions in Wikipedia

Cited by 52 publications

References 21 publications

Neither a Bazaar nor a cathedral: The interplay between structure and agency in Wikipedia's role system

Neither a Bazaar nor a cathedral: The interplay between structure and agency in Wikipedia's role system

Learning To Split and Rephrase From Wikipedia Edit History

Modeling the Relationship between User Comments and Edits in Document Revision

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