Association for Computational Linguistics. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 85–96, Online. Essay Quality Signals as Weak Supervision for Source-based Essay Scoring. Anthology ID: 2021.bea-1.9 Volume: Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications Month: April Year: 2021 Address: Online Venue: BEA SIG: SIGEDU Publisher: Association for Computational Linguistics Note: Pages: 85–96 Language: URL: DOI: Bibkey: zhang-litman-2021-essay Cite (ACL): Haoran Zhang and Diane Litman. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014).Abstract Human essay grading is a laborious task that can consume much time and effort. "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15.1 (2014): 1929-1958.Ĭho, Kyunghyun, et al. In 2014 IEEE Global Conference on Wireless Computing & Networking (GCWCN), pp. ![]() Online Examination with short text matching. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. Nlp based machine learning approaches for text summarization. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. Natural language processing based abstractive text summarization of reviews. ![]() In 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), pp. Extracting semantic knowledge from unstructured text using embedded controlled language. In 2020 24th International Conference Information Visualisation (IV) (pp. Comparison of full-text articles and abstracts for visual trend analytics through natural language processing. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. An ensemble approach for extractive text summarization. ![]() In 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), pp. A Review: Abstractive Text Summarization Techniques using NLP. Shetty, "Sentiment analysis of product reviews: A review," International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017, pp. European Language Resources Association (ELRA), 2018. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. ASAP++: Enriching the ASAP Automated Essay Grading Dataset with Essay Attribute Scores. Sandeep Mathias and PushpakBhattacharyya. Learning Sessions, Stanford University, 2012 – Citeseer "Automated Essay Scoring Using Machine Learning." –Mach. ![]() Association for Computational Linguistics, pages 328–339, 2018. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia. Universal Language Model Fine-tuning for Text Classification.
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