[PDF] Top 20 Erroneous data generation for Grammatical Error Correction

... In this work, we present a novel erroneous data generating method for training English GEC mod- els. Our experiments show that Transformer mod- els pretrained on generated corpus significantly outperform ... See full document

... this data such as de novo genome se- quencing, re-sequencing, and ...successful error correction programs that correct substitution errors include Coral [10], HiTEC [2], Quake [3], Reptile [16], and ... See full document

... that grammatical er- rors interact, for various conceptual and technical reasons, this issue has not been addressed in a sig- nificant way in the ...(1) Data: until very recently we did not have data ... See full document

... approach, error con- fusions are learned automatically via the phrase translation tables extracted from the parallel train- ing ...phrase-level correction that includes both a prepo- sition replacement and ... See full document

... Much recent effort has been devoted to auto- matic evaluation, both within GEC (Napoles et al., 2015; Felice and Briscoe, 2015; Ng et al., 2014; Dahlmeier and Ng, 2012, see §2), and more gen- erally in text-to-text ... See full document

... Having extracted the edits, the next step is to as- sign them error types. While Swanson and Ya- mangil (2012) did this by means of maximum entropy classifiers, one disadvantage of this ap- proach is that such ... 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

... Although the proposed model introduced a tradeoff between precision and recall (Rec.), this tradeoff was tolerable in order to improve the overall F1-score. Since GEC is a task where false alarm is critical, obtaining ... 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

... tuning data drops. To bal- ance the skewed data where samples without errors greatly outnumber samples with errors, we give a higher weight to sample pairs where the decoder proposed a valid ... See full document

... ing data for this shared task. The data con- sist of essays, subdivided into ...not error-free (for example, quotation marks are handled incorrectly in some contexts), we use it to maintain ... 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- ing a ... See full document

... By fixing p ≤ 0.05 we directly evaluate rank- ings of the form given in Table 3. The absolute values of scores and their different interpretations between methods become irrelevant which makes it unnecessary to tune a ... See full document

... the correction provided by the human ...an error greatly out- number the instances that do contain an ...an error and retain a random sample of q percent of the instances that do not contain an ... See full document

... Building error correction systems using machine learning techniques can require a considerable amount of annotated data which is difficult to ob- ...Available error-annotated corpora are often ... See full document

... spelling error detection approachesand correction tools are available in the market all popular ...for correction and detection approach to generate absolute ...an error. In order to rectify ... See full document

... fewer grammatical errors compared to ...the correction of grammatical errors reduces the sentence’s n-gram overlap with the reference sentence, as shown in Table 6 (similar to the phenomenon observed ... See full document

... pseudo data, several deci- sions must be made about the experimental con- figurations, namely, (i) the method of generating the pseudo data, (ii) the seed corpus for the pseudo data, and (iii) the ... See full document

... Generally, for GEC on annotated data such as the NUCLE corpus (Dahlmeier et al., 2013) in this year’s shared task which contains both origi- nal errors and human annotations, there are two main types of ... See full document

... with each other, future versions of the system will all be trained on the same corpus. We also used two lists, one consisting of 64 prepositions and one consisting of 23 determiners, both extracted from the C ON LL-2013 ... See full document