TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation (2022.lrec-1)
Copied to clipboard
Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, Kentaro Inui
| Challenge: | Argumentation and debate are effective tools for developing critical thinking skills, but it requires a lot of time and effort. |
| Approach: | They propose to automate the process of giving diagnostic comments to students . they define criteria for a template set that can be used to evaluate the model . |
| Outcome: | The proposed model can be used to evaluate arguments and evaluate them in real time. |
Similar Papers
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning (D19-3)
Copied to clipboard
Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz, Jan Kiesewetter, Jan Zottmann, Michael Sailer, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych
| Challenge: | Existing systems for technologyenhanced learning address skills on recalling, explaining, and applying knowledge, e.g., in automatically generated language learning exercises and math word problems. |
| Approach: | They propose to leverage a NLP model to support experts in their further data annotation with automatic suggestions and provide automatic feedback for students. |
| Outcome: | The proposed system improves on two user studies on diagnostic reasoning in medicine and teacher education and can be extended to further use cases. |
Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing (2023.findings-eacl)
Copied to clipboard
| Challenge: | Using the essay context of the revision and feedback from students prior to the revision, we identify desirable and undesirable revisions. |
| Approach: | They propose to use the essay context of the revision and the feedback students received before the revision to improve classifier performance. |
| Outcome: | The proposed models improve over baseline models, while models utilizing context improve over the baseline models. |
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support (2023.acl-long)
Copied to clipboard
| Challenge: | assessing whether and how different claims in a text need to be revised is a hard task, especially for novice writers. |
| Approach: | They propose a sampling strategy based on revision distance to capture differences between versions of the same text. |
| Outcome: | The proposed sampling strategy can be done without additional annotations and judgments. |
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays (P18-1)
Copied to clipboard
| Challenge: | Existing work on automated essay scoring has focused on holistic scoring, which summarizes the quality of an essay with a single score. |
| Approach: | They present a corpus of essays simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness. |
| Outcome: | The proposed corpus could trigger the development of novel computational models that provide useful feedback to students on why their arguments are (un)persuasive . |
Template-guided Grammatical Error Feedback Comment Generation (2023.eacl-srw)
Copied to clipboard
| Challenge: | Writing corrective feedback on learner text is widespread in language education, but it can be time-consuming for teachers. |
| Approach: | They propose to use feedback comment generation to generate explanatory notes for learners by categorizing comments and constraining outputs of noisy classes. |
| Outcome: | The proposed scheme can be used to generate feedback comment corpora using a broader scope than existing typologies focused on error correction. |
Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing research on predicting argument quality based on subjective assessments of human annotators ignores this limitation. |
| Approach: | They propose to compare different revisions of the same claim to assess their quality . they use logistic regression and transformer-based neural networks to learn quality indicators . |
| Outcome: | The proposed tasks show that the learned indicators generalize well across topics. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
Copied to clipboard
Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments (L18-1)
Copied to clipboard
| Challenge: | eRulemaking is a way for government agencies to directly reach citizens to solicit their opinions and experiences regarding newly proposed rules. |
| Approach: | They propose an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. |
| Outcome: | The proposed corpus contains 731 user comments on consumer debt collection practices rule by the Consumer Financial Protection Bureau. |
A Corpus for Argumentative Writing Support in German (2020.coling-main)
Copied to clipboard
| Challenge: | In today's world most information is readily available. Consequently, the sole reproduction of information is losing attention. |
| Approach: | They propose an annotation approach to capture claims and premises of arguments and their relations in student-written peer reviews on business models in german language. |
| Outcome: | The proposed annotation scheme guides annotators to moderate agreement with the proposed scheme on 50 persuasive student-written peer reviews on business models. |
Dataset and Baseline for Automatic Student Feedback Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Currently, student feedback is collected manually, but it does not indicate the student's opinion on different aspects of the teaching/learning process. |
| Approach: | They propose to annotate student feedback corpus which contains 3000 instances . they propose a hierarchical taxonomy for aspect categorization, which covers all areas . |
| Outcome: | The proposed model can be used for aspects analysis, document level sentiment analysis and document level analysis. |