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Shamil Chollampatt

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3 papers

1 author row

AAAI Conference 2018 Conference Paper

A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

Shamil Chollampatt
Hwee Tou Ng

We improve 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 Ngram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JF- LEG), 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.

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IJCAI Conference 2016 Conference Paper

Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction

Duc Tam Hoang
Shamil Chollampatt
Hwee Tou Ng

Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners. The statistical machine translation (SMT) approach to GEC, in which sentences written by second language learners are translated to grammatically correct sentences, has achieved state-of-the-art accuracy. However, the SMT approach is unable to utilize global context. In this paper, we propose a novel approach to improve the accuracy of GEC, by exploiting the n-best hypotheses generated by an SMT approach. Specifically, we build a classifier to score the edits in the n-best hypotheses. The classifier can be used to select appropriate edits or re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC system that uses the SMT approach. Our experiments show that our methods achieve statistically significant improvements in accuracy over the best published results on a benchmark test dataset on GEC.

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IJCAI Conference 2016 Conference Paper

Neural Network Translation Models for Grammatical Error Correction

Shamil Chollampatt
Kaveh Taghipour
Hwee Tou Ng

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.

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