Language Acquisition
55 papers with code • 1 benchmarks • 6 datasets
Language acquisition refers to tasks related to the learning of a second language.
Benchmarks
These leaderboards are used to track progress in Language Acquisition
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Libraries
Use these libraries to find Language Acquisition models and implementationsDatasets
BLiMP
Duolingo SLAM Shared Task
Duolingo Spaced Repetition Data
New Brown Corpus
BabySLM
Most implemented papers
Semantic speech retrieval with a visually grounded model of untranscribed speech
We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query.
Predicting and Explaining Human Semantic Search in a Cognitive Model
Recent work has attempted to characterize the structure of semantic memory and the search algorithms which, together, best approximate human patterns of search revealed in a semantic fluency task.
Interactive Grounded Language Acquisition and Generalization in a 2D World
PaddlePaddle/XWorld
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ICLR 2018
We build a virtual agent for learning language in a 2D maze-like world.
Neural Network Acceptability Judgments
nyu-mll/CoLA-baselines
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TACL 2019
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence.
Context Based Approach for Second Language Acquisition
Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en).
Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models
toizzy/tilt-transfer
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EMNLP 2020
We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models.
Visually Grounded Continual Learning of Compositional Phrases
To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes.
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the input was generated by concatenating words from a fixed but unknown lexicon.
Learning Semantic Correspondences with Less Supervision
A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state.
