Language Acquisition

BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. Aggregate human agreement with the labels is 96.4%. Source: BLiMP Image Source: https://arxiv.org/pdf/1912.00582v3.pdf

IGLU is a dataset designed for interactive grounded language understanding. It has a total of 8,136 single-turn data pairs of instructions and actions. Every single sample is randomly initialized with a pre-built structure from previously collected multi-turn interactions data. Source: Collecting Interactive Multi-modal Datasets for Grounded Language Understanding Image Source: https://arxiv.org/pdf/2211.06552v1.pdf

This repository contains gzipped files containing more than 2 million tokens (words) from answers submitted by more than 6,000 students over the course of their first 30 days of using Duolingo. It also contains baseline starter code written in Python. There are three data sets, corresponding to three different language courses. More details on the data set and task are available at: http://sharedtask.duolingo.com. (2018-01-10)

This is a gzipped CSV file containing the 13 million Duolingo student learning traces used in experiments by Settles & Meeder (2016). For more details and replication source code, visit: https://github.com/duolingo/halflife-regression (2016-06-07)

BabySLM is a language-acquisition-friendly benchmark to probe speech-based LMs at the lexical and syntactic levels, both of which are compatible with the vocabulary typical of children's language experiences. Source: BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models Image Source: BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models

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A new dataset for training and evaluating grounded language models. Source: A Visuospatial Dataset for Naturalistic Verb Learning

The first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. Source: A Portuguese Native Language Identification Dataset