[PDF] Top 20 Data Weighted Training Strategies for Grammatical Error Correction

... target data (see Figure 2 and Table ...pretraining data via ∆ppl ...pretraining data (≈ 8B ...scored training stages outperform the unscored models, though by less than if directly comparing ... See full document

... Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet su- perseded more traditional symbolic ...annotated training ... See full document

... re-annotating training data, and then building new NMT models using this newly annotated data as pro- posed by Luong et ...any data and train only one NMT model. Due to the nature of ... See full document

... Grammatical Error Correction (GEC) has been recently modeled using the sequence- to-sequence ...Both strategies yield sim- ilar sized parallel corpora containing around 4B ...to data ... See full document

... Most neural approaches to automatic grammati- cal error correction (GEC) require error-labelled training data to achieve their best performance. Unfortunately, such resources are not ... See full document

... of training data (learner or native) and the algorithm ...English data, because the learned models do not know any- thing about the errors learners ...native data can use the au- thor’s word ... See full document

... in grammatical error cor- rection (Ng et ...grammar correction model is based on a tree-to-string transducer that is specified by a set of rules that each rewrite a tree fragment to a string of words ... See full document

... three error types (ArtOrDet, Prep and Nn) and try several ratios of with-without to find out whether this ratio has great impact on the final result and which ratio performs ...80%-10%-10% data (mentioned ... See full document

... The third approach is to create synthetic corpus from a clean monolingual corpus and use it as ad- ditional data for training. Noise is typically intro- duced either by rule-based substitutions or by us- ... See full document

... ceived comparatively less attention. Brockett et al. (2006) use an SMT system to correct errors in- volving mass noun errors. Because no large anno- tated learner corpus was available, the training data was ... See full document

... The training data provided in our shared task is the NUCLE corpus, the NUS Corpus of Learner English (Dahlmeier et ...matical error correction, since it prevents com- parative evaluations on a ... See full document

... In contrast with phrase-based translation models, factored models make use of additional linguistic clues to guide the system such that it generates translated sentences in which morphological and syntactic constraints ... See full document

... The training data provided in our shared task is the NUCLE corpus, the NUS Corpus of Learner English (Dahlmeier et ...matical error correction, since it prevents com- parative evaluations on a ... See full document

... predictions, a random sample of 500 structures of each type from the training data was examined by a human annotator with formal training in Linguis- tics. The human annotations were then compared ... See full document

... To address the issues, we employ a neural encoder-decoder GEC model with a reinforcement learning approach in which we directly optimize the model toward our final objective (i.e., evalua- tion metric). The objective of ... See full document

... only training data in the form of raw or syntactically labeled text) are very good guessers when it comes to detecting and correcting collocations, idi- oms, typos and small grammatical inad- ... See full document

... to grammatical error correc- tion – machine learning classification and machine ...through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of ... See full document

... during training is the correction provided by the human ...During training, the instances that do not contain an error greatly out- number the instances that do contain an ...an error ... See full document

... fit training of a single convolutional encoder-decoder model efficiently in a single Titan X GPU with 12 GB memory, we restrict each batch to a maxi- mum of 6,000 source or target tokens per batch, apart from ... See full document

... Other recent work focuses on improving model inference. Ge et al. (2018a) proposed correcting a sentence more than once through multi-round model inference. Lichtarge et al. (2018) introduced iterative decoding to ... See full document