Language Acquisition
69 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
TMU System for SLAM-2018
kanekomasahiro/SLAM18_model
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WS 2018
We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018).
Deep Factorization Machines for Knowledge Tracing
jilljenn/ktm
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WS 2018
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
Measuring language distance among historical varieties using perplexity. Application to European Portuguese.
In our approach, we used a perplexity-based measure to calculate language distance between all the historical periods of a specific language: European Portuguese.
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions
Inter-annotator agreement is given separately for prepositions and verbs, and for adult speech and child speech.
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem
The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones.
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
Unsupervised Mining of Analogical Frames by Constraint Satisfaction
It has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic.
The Second DIHARD Diarization Challenge: Dataset, task, and baselines
This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned.
