  We present DaLAJ 1.0, a Dataset for Linguistic Acceptability Judgments for
Swedish, comprising 9 596 sentences in its first version; and the initial
experiment using it for the binary classification task. DaLAJ is based on the
SweLL second language learner data, consisting of essays at different levels of
proficiency. To make sure the dataset can be freely available despite the GDPR
regulations, we have sentence-scrambled learner essays and removed part of the
metadata about learners, keeping for each sentence only information about the
mother tongue and the level of the course where the essay has been written. We
use the normalized version of learner language as the basis for the DaLAJ
sentences, and keep only one error per sentence. We repeat the same sentence
for each individual correction tag used in the sentence. For DaLAJ 1.0 we have
used four error categories (out of 35 available in SweLL), all connected to
lexical or word-building choices. Our baseline results for the binary
classification show an accuracy of 58% for DaLAJ 1.0 using BERT embeddings. The
dataset is included in the SwedishGlue (Swe. SuperLim) benchmark. Below, we
describe the format of the dataset, first experiments, our insights and the
motivation for the chosen approach to data sharing.
