We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw, unstructured text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.
We view the WikiAtomicEdits corpus as being especially valuable for ongoing work in sentence representation and generation, which requires models of what good'' sentences look like and how they are constructed. Recent work has attempted to model sentence generation by re-writing existing sentences, either using crowdsourced edit examples @cite_22 or unsupervised heuristics @cite_15 ; in contrast, we provide a large corpus of natural, human-produced edits.
@cite_15 @cite_22
43022990 2110737727 1858383477 78382366
This paper introduces the freely available WikEd Error Corpus. We describe the data mining process from Wikipedia revision histories, corpus content and format. The corpus consists of more than 12 million sentences with a total of 14 million edits of various types. As one possible application, we show that WikEd can be successfully adapted to improve a strong baseline in a task of grammatical error correction for English-as-a-Second-Language (ESL) learners’ writings by 2.63 . Used together with an ESL error corpus, a composed system gains 1.64 when compared to the ESL-trained system. We present a novel approach for rapidly developing a corpus with discourse annotations using crowdsourcing. Although discourse annotations typically require much time and cost owing to their complex nature, we realize discourse annotations in an extremely short time while retaining good quality of the annotations by crowdsourcing two annotation subtasks. In fact, our experiment to create a corpus comprising 30,000 Japanese sentences took less than eight hours to run. Based on this corpus, we also develop a supervised discourse parser and evaluate its performance to verify the usefulness of the acquired corpus. We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone. Naturally-occurring instances of linguistic phenomena are important both for training and for evaluating automatic text processing. When available in large quantities, they also prove interesting material for linguistic studies. In this article, we present WiCoPaCo (Wikipedia Correction and Paraphrase Corpus), a new freely-available resource built by automatically mining Wikipedia’s revision history. The WiCoPaCo corpus focuses on local modifications made by human revisors and include various types of corrections (such as spelling error or typographical corrections) and rewritings, which can be categorized broadly into meaning-preserving and meaning-altering revisions. We present an initial hand-built typology of these revisions, but the resource allows for any possible annotation scheme. We discuss the main motivations for building such a resource and describe the main technical details guiding its construction. We also present applications and data analysis on French and report initial results on spelling error correction and morphosyntactic rewriting. The WiCoPaCo corpus can be freely downloaded from http: wicopaco.limsi.fr.