Neural-based Text Error Correction (TEC) Algorithm
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A Neural-based Text Error Correction (TEC) Algorithm is a text error correction algorithm that is a neural NLP algorithm.
- Context:
- It can be implemented by a Neural TEC System.
- It can be a Neural Grammatical Error Correction Algorithm, a Neural Orthographic Error Correction Algorithm, ...
- It can range from being a Character-Level Neural TEC Algorithm to being a Word-Level Neural TEC Algorithm.
- It can range from being a Language Model-based Neural TEC Algorithm to being a seq2seq Neural-based TEC Algorithm.
- Example(s):
- Counter-Example(s):
- See: Text Error Correction.
References
2018
- (Chollampatt & Ng, 2018) ⇒ Shamil Chollampatt, and Hwee Tou Ng. (2018). “A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction.” In: Proceedings of the Thirty-Second Conference on Artificial Intelligence (AAAI-2018).
- QUOTE: We improve automatic automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character N-gram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior neural approaches on this task as well as strong statistical machine translation-based systems with neural and task-specific features trained on the same data. Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory (LSTM) networks in capturing the local context via attention, and thereby improving the coverage in correcting grammatical errors. By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.
2016
- (Xie et al., 2016) ⇒ Ziang Xie, Anand Avati, Naveen Arivazhagan, Dan Jurafsky, and Andrew Y. Ng. (2016). “Neural Language Correction with Character-Based Attention.” In: CoRR, abs/1603.09727.
- QUOTE: Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism. ...