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3 results for “SemEval-2021”
SemEval-2021 Task 12: Learning with Disagreements
<p>This repository contains the Post-Evaluation data for SemEval-2021 Task 12: Learning with Disagreement, a shared task on learning to classify with datasets containing disagreements. </p> <p>The aim of this shared task is to provide a unified testing framework for learning from disagreements using the best-known datasets containing information about disagreements for interpreting language and classifying images:</p> <p> 1. LabelMe-IC: Image Classification using a subset of LabelMe images (Russell et al., 2008), is a widely used, community-created image classification dataset where images are assigned to one of 8 categories: highway, inside city, tall building, street, forest, coast, mountain, open country. Rodrigues and Pereira (2017) collected crowd labels for these images using Amazon Mechanical Turk (AMT).</p> <p> 2. CIFAR10-IC: Image Classification using a subset of CIFAR-10 dataset, https://www.cs.toronto.edu/~kriz/cifar.html. The entire dataset consists of colour images in 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck). Crowdsourced labels for this dataset were collected by Peterson et al (2019).</p> <p> 3. PDIS: Information Status Classification using Phrase Detectives Information. Information Status Classification (IS) in Phrase Detectives (Poesio et al., 2019) dataset involves identifying the information status of a noun phrase: whether that noun phrase refers to new information or to old information.</p> <p> 4. Gimpel-POS: Part-of-Speech tagging using the Gimpel dataset (Gimpel et al., 2011) for Twitter posts. Plank et al.(2014b) mapped the Gimpel tags to the universal tag set (Petrov et al., 2011), using these tags as gold, and collected crowdsourced labels.</p> <p> 5. Humour: ranking one-line texts using pairwise funniness judgements (Simpson et al., 2019). Crowdworkers have annotated pairs of puns to indicate which is funniest. A gold standard ranking was produced using a large number of redundant annotations. The goal is to infer the gold standard ranking from a reduced number of crowdsourced judgements.</p> <p><br> The files contained in this data collection are as follows:<br> starting_kit.zip - Base models used provided for the shared task. <br> practice_phase_data.zip - The training and development data used during the Practice Phase of the competition. <br> test_phase_data.zip - The test data, used during the Evaluation Phase of the competition</p> <p>Details of format of each dataset for each task can be found on <a href="http://This data collection contains the post-evaluation data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection: This repository contains the Post-Evaluation data for SemEval-2021 Task 12: Learning with Disagreement, a shared task on learning to classify with datasets containing disagreements. The aim of this shared task is to provide a unified testing framework for learning from disagreements using the best-known datasets containing information about disagreements for interpreting language and classifying images: 1. LabelMe-IC: Image Classification using a subset of LabelMe images (Russell et al., 2008), is a widely used, community-created image classification dataset where images are assigned to one of 8 categories: highway, inside city, tall building, street, forest, coast, mountain, open country. Rodrigues and Pereira (2017) collected crowd labels for these images using Amazon Mechanical Turk (AMT). 2. CIFAR10-IC: Image Classification using a subset of CIFAR-10 dataset, https://www.cs.toronto.edu/~kriz/cifar.html. The entire dataset consists of colour images in 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck). Crowdsourced labels for this dataset were collected by Peterson et al (2019). 3. PDIS: Information Status Classification using Phrase Detectives Information. Information Status Classification (IS) in Phrase Detectives (Poesio et al., 2019) dataset involves identifying the information status of a noun phrase: whether that noun phrase refers to new information or to old information. 4. Gimpel-POS: Part-of-Speech tagging using the Gimpel dataset (Gimpel et al., 2011) for Twitter posts. Plank et al.(2014b) mapped the Gimpel tags to the universal tag set (Petrov et al., 2011), using these tags as gold, and collected crowdsourced labels. 5. Humour: ranking one-line texts using pairwise funniness judgements (Simpson et al., 2019). Crowdworkers have annotated pairs of puns to indicate which is funniest. A gold standard ranking was produced using a large number of redundant annotations. The goal is to infer the gold standard ranking from a reduced number of crowdsourced judgements. The files contained in this data collection are as follows: starting_kit.zip - Base models used provided for the shared task. practice_phase_data.zip - The training and development data used during the Practice Phase of the competition. test_phase_data.zip - The test data, used during the Evaluation Phase of the competiton Details of format of each dataset for each task can be found here: https://competitions.codalab.org/competitions/25748#participate-get_data">Codalab</a>.</p>
SemEval-2021 Task 1: Lexical Complexity Prediction
<p>We ran a shared task on Lexical Complexity Prediction via SemEval using this data. The data was released according to the following schedule:</p> <ul> <li> <p>Trial data available: July 31, 2020</p> </li> <li> <p>Training data available: September 4, 2020</p> </li> <li> <p>Test data available/Evaluation starts: January 11, 2021</p> </li> </ul> <p>See the task website for further information: <a href="https://sites.google.com/view/lcpsharedtask2021">https://sites.google.com/view/lcpsharedtask2021</a></p> <p>See the CodaLab site to view submissions: <a href="https://competitions.codalab.org/competitions/27420">https://competitions.codalab.org/competitions/27420</a></p> <p>If you find this dataset useful, please cite the task paper as: <em>Shardlow, M. Evans, R., Paetzold, G and Zampieri, M. SemEval-2021 Task 1: Lexical Complexity Prediction. In Proceedings of the 15th International Workshop on Semantic Evaluation. 2021.</em></p> <p>The script evaluate.py is the same evaluation script that was used as part of the shared task. You can run this by calling "python evaluate.py <results_path>/res/ <reference_path>/ref/" where res and ref are directories containing the system's results and the reference labels respectively.</p> <p>The trial, training and test data are available. The trial data comprises of 99 MWEs (29 bible, 33 biomed and 37 europarl) and 421 single word instances (143 bible, 135 biomed and 143 europarl).</p> <p>The training data comprises of 1,517 MWEs (505 bible, 514 biomed and 498 europarl) and 7,662 single word instances (2,574 bible, 2,576 biomed and 2,512 europarl).</p> <p>The test data comprises of 184 MWEs (66 bible, 53 biomed and 65 europarl) and 917 single word instances (283 bible, 289 biomed and 345 europarl). It is compressedd and encrypted using 7zip as above. The password will be released to those registered for the task. The order of the test data has been randomised to prevent systems from overfitting to the data ordering. All submissions should be made via CodaLab as above.</p> <p>The trial and training data is arranged by token and sorted by complexity (i.e., instances with the same token appear together in groups, and the groups are sorted by the complexity of the lowest scored item). Tokens that appear in one partition will not appear in another partition. We have deliberately included more than one instance of a token where possible to identify places where the context affects the complexity of a word. Consider, for example, the following two sentences from the trial data:</p> <ul> <li>We now have a proposal on the <em>table</em> which lays down strict emissions values for the next ten years and which simultaneously creates clarity and incentives for technological innovations.</li> <li>Mr President, in coordination with other groups, I would like to <em>table</em> an oral amendment concerning the draft bill in the Duma to ignore certain rulings of the European Court of Human Rights.</li> </ul> <p>When <em>table</em> is used as a noun in the first sentence (albeit in an abstract sense) it receives a score of 0.01 indicating it is very easily understood. However, when it is used as a verb in the second, it is scored at 0.23, indicating a more difficult word to comprehend.</p> <p>The data comes from three sources: biblical text, biomedical articles and proceedings of the European Parliament. These sources were selected as they contain a natural mixture of common language and difficult to understand expressions, whilst each containing vastly different domain-specific vocabulary.</p>
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
<p>Data sharing restrictions are common in NLP datasets. For example, Twitter policies do not allow sharing of tweet text, though tweet IDs may be shared. The situation is even more common in clinical NLP, where patient health information must be protected, and annotations over health text, when released at all, often require the signing of complex data use agreements. The SemEval-2021 Task 10 framework asks participants to develop semantic annotation systems in the face of data sharing constraints. A participant's goal is to develop an accurate system for a target domain when annotations exist for a related domain but cannot be distributed. Instead of annotated training data, participants are given a model trained on the annotations. Then, given unlabeled target domain data, they are asked to make predictions.</p> <p>Website: <a href="https://machine-learning-for-medical-language.github.io/source-free-domain-adaptation/">https://machine-learning-for-medical-language.github.io/source-free-domain-adaptation/</a></p> <p>CodaLab site: <a href="https://competitions.codalab.org/competitions/26152">https://competitions.codalab.org/competitions/26152</a></p> <p>Github repository: <a href="https://github.com/Machine-Learning-for-Medical-Language/source-free-domain-adaptation">https://github.com/Machine-Learning-for-Medical-Language/source-free-domain-adaptation</a></p>
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