| Challenge: | Existing treebanks are limited in size, genre, and topic coverage, making manual annotation time-consuming and expensive. |
| Approach: | They propose a web-based interactive tool for editing dependency trees that uses machine learning to accelerate annotation. |
| Outcome: | CROWDTREE is a web-based interactive tool for editing dependency trees . it can train a parsing model during the annotation process and can even be compatible with Mechanical Turk. |
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Dependency Tree Annotation with Mechanical Turk (D19-59)
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| Challenge: | a recent study shows that crowdsourcing is often used to obtain linguistic annotations but is rarely used for parsing. |
| Approach: | They propose to use Mechanical Turk to crowdsource parse trees using an interactive graphical dependency tree editor. |
| Outcome: | The proposed method is the first published use of Mechanical Turk to crowdsource parse trees . the authors find that the workers achieve high levels of accuracy on 72% of the sentences . |
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation (D19-59)
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| Challenge: | Recent advances in machine reading and listening comprehension involve the annotation of long texts. |
| Approach: | They propose a way to perform a sentence-by-sentence annotation task with crowd annotators. |
| Outcome: | The proposed approach can be used to identify claims in a debate speech. |
Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
| Approach: | They propose a stack-pointer network-based dependency parser that reads whole sentence and builds dependency tree top-down in a depth-first fashion. |
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Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer (L18-1)
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| Challenge: | Using annotated corpus for linguistic purposes is no longer justified . hand-crafted syntactic resources such as grammars and lexicons can be used as sources of features to guide data driven systems. |
| Approach: | They propose an efficient method for transferring annotations between two different treebanks of the same language. |
| Outcome: | The proposed method is based on the Universal Dependency annotation scheme and was evaluated on the gold standard (94.75% of LAS, 99.40% UAS on the test set). |
A Gold Standard Dependency Treebank for Turkish (2020.lrec-1)
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| Challenge: | Currently, Turkish treebanks are limited due to the limited number of annotated sentences in the domains of Wikipedia and ITU Web Treebanks. |
| Approach: | They propose to annotate Turkish web and Wikipedia sentences for segmentation, morphology, part-of-speech and dependency relations using tagsets and a Wikipedia section. |
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Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
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| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
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A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
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| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
| Outcome: | The proposed method improves on training with multiple treebanks for a single language. |
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)
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| Challenge: | a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time . |
| Approach: | They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors . |
| Outcome: | The proposed method can predict treebank vectors for sentences that do not come from a treebank used in training with sufficient accuracy for nine out of ten languages. |
When Collaborative Treebank Curation Meets Graph Grammars (2020.lrec-1)
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| Challenge: | Arborator-Grew is a collaborative annotation tool for treebank development. |
| Approach: | They present a collaborative annotation tool for treebank development that combines the features of Arborator and Grew. |
| Outcome: | The proposed tool is a complete redevelopment and modernization of Arborator, replacing its internal database storage by a new Grew API. |