Understanding search queries is a hard problem as it involves dealing with "word salad" text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7 on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.
We have referenced much of the related work throughout the paper. We now review another orthogonally related field of work. Grammatical error correction (GEC) is the task of correcting the grammatical errors (if any) in a piece of text @cite_10 . As GEC includes not just identification of ungrammatical text but also correcting the text to produce grammatical text, its a more complex task. However, grammatical error prediction @cite_15 @cite_12 is the task of classifying whether or not a sentence is grammatical, which is more closely related to our task as classifying a question as well-formed requires making judgement on both the style and grammar of the text.
@cite_15 @cite_10 @cite_12
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Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography. Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning generates diverse error-corrected sentence pairs during training, enabling the error correction model to learn how to improve a sentence's fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps. Combining fluency boost learning and inference with convolutional seq2seq models, our approach achieves the state-of-the-art performance: 75.72 (F_ 0.5 ) on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set respectively, becoming the first GEC system that reaches human-level performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks. The CoNLL-2014 shared task was devoted to grammatical error correction of all error types. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results. Compared to the CoNLL2013 shared task, we have introduced the following changes in CoNLL-2014: (1) A participating system is expected to detect and correct grammatical errors of all types, instead of just the five error types in CoNLL-2013; (2) The evaluation metric was changed from F1 to F0.5, to emphasize precision over recall; and (3) We have two human annotators who independently annotated the test essays, compared to just one human annotator in CoNLL-2013. In this paper, we consider the problem of generating candidate corrections for the task of correcting errors in text. We focus on the task of correcting errors in preposition usage made by non-native English speakers, using discriminative classifiers. The standard approach to the problem assumes that the set of candidate corrections for a preposition consists of all preposition choices participating in the task. We determine likely preposition confusions using an annotated corpus of non-native text and use this knowledge to produce smaller sets of candidates. We propose several methods of restricting candidate sets. These methods exclude candidate prepositions that are not observed as valid corrections in the annotated corpus and take into account the likelihood of each preposition confusion in the non-native text. We find that restricting candidates to those that are observed in the non-native data improves both the precision and the recall compared to the approach that views all prepositions as possible candidates. Furthermore, the approach that takes into account the likelihood of each preposition confusion is shown to be the most effective.