Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; , 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (, 2018) and BERT (, 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models' performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.
The Corpus of Linguistic Acceptability @cite_14 is a dataset of 10k example sentences including expert annotations for grammatical acceptability. The sentences are example sentences taken from 23 theoretical linguistics publications, mostly about syntax, including undergraduate textbooks, research articles, and dissertations. Such example sentences are usually labeled for acceptability by their authors or a small group of native English speakers. A small random sample of the CoLA development set (with our added annotations) can be seen in Table .
@cite_14
2806120502 2152928267 2461928176 43022990
In this work, we explore the ability of artificial neural networks to judge the grammatical acceptability of a sentence. Machine learning research of this kind is well placed to answer important open questions about the role of prior linguistic bias in language acquisition by providing a test for the Poverty of the Stimulus Argument. In service of this goal, we introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical by expert linguists. We train several recurrent neural networks to do binary acceptability classification. These models set a baseline for the task. Error-analysis testing the models on specific grammatical phenomena reveals that they learn some systematic grammatical generalizations like subject-verb-object word order without any grammatical supervision. We find that neural sequence models show promise on the acceptability classification task. However, human-like performance across a wide range of grammatical constructions remains far off. In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words. In example (1), a sentence from a Wall Street Journal article taken from the Penn TreeBank corpus is further segmented into four EDUs, (1a), (1b), (1c) and (1d) (RST, 2002). Discourse segmentation, clearly, is not as easy as sentence boundary detection. The lack of consensus with regards to what constitutes an elementary discourse unit adds to the difficulty. Building a rule based discourse segmenter can be a tedious task since these rules would have to be based on the underlying grammar of the particular parser that is to be used. Therefore, we adopted a neural network model for automatically building a discourse segmenter from an underlying corpus of segmented text. We chose to use part-of-speech tags, syntactic information, discourse cues and punctuation. Our ultimate goal is to build a discourse parser that uses this discourse segmenter. The data that we used to train and test our discourse segmenter is the RST-DT (RST, 2002) corpus. The corpus contains 385 Wall Street Journal articles from the Penn Treebank. The training set consists of 347 articles for a total of 6132 sentences, whilst the test set contains 38 articles for a total of 991 sentences. The RST-DT corpus provides us with pairs of sentences and EDUs. For the syntactic structure of the sentences, we have used both the gold standard Penn Treebank data and syntactic parse trees generated by (Charniak, 2000). As regards the discourse cues, we used a list of 168 possible discourse markers. 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.