Challenge: Large-scale pretrained language models have brought substantial advances to the natural language processing field.
Approach: They present an internationalized annotation and human evaluation bundle, called Textinator, along with documentation and video tutorials.
Outcome: The proposed tool is compared to other tools along 9 different axes and is available in multiple languages.

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A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)

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Challenge: This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field.
Approach: This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement .
Outcome: This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field.
FITAnnotator: A Flexible and Intelligent Text Annotation System (2021.naacl-demos)

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Challenge: In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation.
Approach: They propose a generic web-based tool for efficient text annotation.
Outcome: The proposed tool is based on a fully modular architecture and provides three kinds of interfaces to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers.
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation (2022.coling-1)

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Challenge: Natural language generation models are a key component of deep learning, says aaron eliott . he says it is crucial to develop and apply better metrics for NLG evaluation .
Approach: a new open-source library for NLG evaluation is created to facilitate researchers to judge the effectiveness of their models. the framework provides a living collection of NLG metrics in a unified and easy-to-use environment.
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Cross-lingual Semantic Representation for NLP with UCCA (2020.coling-tutorials)

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Challenge: introductory tutorial to UCCA, a symbolic meaning representation for semantic representations.
Approach: This tutorial introduces UCCA, a cross-linguistically applicable framework for semantic representation . it will provide a detailed introduction to the UCca annotation guidelines, design philosophy and available resources .
Outcome: The tutorial will provide a detailed introduction to the UCCA framework and compare it to other meaning representations.
HUMAN: Hierarchical Universal Modular ANnotator (2020.emnlp-demos)

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Challenge: HUMAN is a web-based annotation tool that covers a variety of annotation tasks on textual and image data.
Approach: They propose a web-based annotation tool that covers a variety of annotation tasks on textual and image data.
Outcome: HUMAN covers a variety of annotation tasks on textual and image data and uses an internal deterministic state machine to chain different tasks in an interdependent manner.
On the Limitations of Reference-Free Evaluations of Generated Text (2022.emnlp-main)

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Challenge: a recent study has shown that evaluation metrics which accurately estimate the quality of generated text are limited in their ability to evaluate generated text.
Approach: They argue that reference-free metrics are limited in their ability to evaluate generated text . they recommend that they be used as diagnostic tools for analyzing and understanding model behavior .
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TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts (2020.lrec-1)

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Challenge: Existing annotation tools are not efficient for the annotation of corpora and are not error-free.
Approach: They propose to extend existing annotation tools by evaluating their flexibility and efficiency.
Outcome: The proposed system performs platform-independent multimodal annotations and annotates complex textual structures.
LightTag: Text Annotation Platform (2021.emnlp-demo)

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Challenge: LightTag is a text annotation tool built on the premise of global optimization by addressing annotator as well as project managers and data scientists who manage the work and enforce production quality.
Approach: They propose to use LightTag to optimize the global NLP process by addressing annotators as well as project managers and data scientists who manage the work and enforce production quality.
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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

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Challenge: Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach: They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome: The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.
Geo-Cultural Representation and Inclusion in Language Technologies (2024.lrec-tutorials)

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Challenge: audi et al.: training and evaluation of language models rely on semi-structured data that is annotated by humans . e-learning tools do not integrate rich and diverse community perspectives into language technologies .
Approach: They will examine how different socio-cultural perspectives influence what is taken as ground truth by models.
Outcome: This tutorial examines how different socio-cultural perspectives influence representations of global concepts.

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