Language Acquisition

47 papers with code • 1 benchmarks • 5 datasets

Language acquisition refers to tasks related to the learning of a second language.

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Use these libraries to find Language Acquisition models and implementations
2 papers
106

Most implemented papers

Semantic speech retrieval with a visually grounded model of untranscribed speech

kamperh/semantic_flickraudio 5 Oct 2017

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

FilipMiscevic/random_walk WS 2018

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.

Context Based Approach for Second Language Acquisition

iampuntre/slam18 WS 2018

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

flowersteam/Imagine 21 Feb 2020

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 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

INK-USC/VG-CCL EMNLP 2020

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

kamperh/dpdp_aernn 12 May 1999

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

worksheets/0xd8ae7710 1 Aug 2009

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.