Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning

• URL: http://arxiv.org/abs/2306.01584v2
• Date: Thu, 15 Jun 2023 10:23:53 GMT
• Title: Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning
• Abstract summary: We focus on automatic creation of gapfilling exercises for language learning, specifically grammar exercises. We contribute (i) a novel neural network architecture specifically designed for aforementioned gap-filling exercise generation task, and (ii) a real-world benchmark dataset for French grammar. Our model implementation and the dataset are made publicly available to foster future research, thus offering a standardized evaluation and baseline solution of the proposed partially annotated data prediction task in grammar exercise creation.
• Score: 19.43668931500507
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Since performing exercises (including, e.g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher, there is a great value in automatic exercise generation in digital tools in education. In this paper, we particularly focus on automatic creation of gapfilling exercises for language learning, specifically grammar exercises. Since providing any annotation in this domain requires human expert effort, we aim to avoid it entirely and explore the task of converting existing texts into new gap-filling exercises, purely based on an example exercise, without explicit instruction or detailed annotation of the intended grammar topics. We contribute (i) a novel neural network architecture specifically designed for aforementioned gap-filling exercise generation task, and (ii) a real-world benchmark dataset for French grammar. We show that our model for this French grammar gap-filling exercise generation outperforms a competitive baseline classifier by 8% in F1 percentage points, achieving an average F1 score of 82%. Our model implementation and the dataset are made publicly available to foster future research, thus offering a standardized evaluation and baseline solution of the proposed partially annotated data prediction task in grammar exercise creation.

Related papers

• Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets [1.734165485480267]
  We propose a new tool for automatically annotating text using written guidelines without providing training samples. Our results show that the prompt-based approach is comparable with the fine-tuned BERT but without any annotated training data. Our findings emphasize the ongoing paradigm shift in the NLP landscape, i.e., the unification of downstream tasks and elimination of the need for pre-labeled training data.
  arXiv  Detail & Related papers  (2024-06-26T10:44:02Z)
• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• Large Language Model Augmented Exercise Retrieval for Personalized Language Learning [2.946562343070891]
  We find that vector similarity approaches poorly capture the relationship between exercise content and the language that learners use to express what they want to learn. We leverage the generative capabilities of large language models to bridge the gap by synthesizing hypothetical exercises based on the learner's input. Our approach, which we call mHyER, overcomes three challenges: (1) lack of relevance labels for training, (2) unrestricted learner input content, and (3) low semantic similarity between input and retrieval candidates.
  arXiv  Detail & Related papers  (2024-02-08T20:35:31Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP [0.5482532589225552]
  In our work we suggest to leverage pretrained language models for training data acquisition. We create a custom dataset which we use to train a medical NER model for German texts, GPTNERMED.
  arXiv  Detail & Related papers  (2022-08-30T18:42:55Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Machine Translation Pre-training for Data-to-Text Generation -- A Case Study in Czech [5.609443065827995]
  We study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. We find that pre-training lets us train end-to-end models with significantly improved performance.
  arXiv  Detail & Related papers  (2020-04-05T02:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.