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Found 18 papers, 1 papers with code
Assessing Grammatical Correctness in Language Learning
We approach the problem with the methods for grammatical error detection (GED), since we hypothesize that models for detecting grammatical mistakes can assess the correctness of potential alternative answers in a learning setting.
Semi-automatically Annotated Learner Corpus for Russian
We present ReLCo— the Revita Learner Corpus—a new semi-automatically annotated learner corpus for Russian.
Tools for supporting language learning for Sakha
Revita is a freely available online language learning platform for learners beyond the beginner level.
Applying Gamification Incentives in the Revita Language-learning System
Our goal is to keep the learner engaged in long practice sessions over many months—rather than for the short-term.
Implicit assessment of language learning during practice as accurate as explicit testing
Exhaustive testing across a wide range of skills can provide a detailed picture of proficiency, but may be undesirable for a number of reasons.
Probing the Category of Verbal Aspect in Transformer Language Models
We perform probing using BERT and RoBERTa on alternative and non-alternative contexts.
GPT-3.5 for Grammatical Error Correction
This paper investigates the application of GPT-3. 5 for Grammatical Error Correction (GEC) in multiple languages in several settings: zero-shot GEC, fine-tuning for GEC, and using GPT-3. 5 to re-rank correction hypotheses generated by other GEC models.
Ranked #5 on
Grammatical Error Correction
on UA-GEC
What do Transformers Know about Government?
This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models. In particular, we explore how BERT encodes the government relation between constituents in a sentence.
Effects of sub-word segmentation on performance of transformer language models
Lastly, we show 4. that LMs of smaller size using morphological segmentation can perform comparably to models of larger size trained with BPE -- both in terms of (1) perplexity and (3) scores on downstream tasks.
Linguistic Constructs as the Representation of the Domain Model in an Intelligent Language Tutoring System
This paper presents the development of an AI-based language learning platform Revita.
Toward a Paradigm Shift in Collection of Learner Corpora
We present the first version of the longitudinal Revita Learner Corpus (ReLCo), for Russian.
Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet
In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet.
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved.
v-trel: Vocabulary Trainer for Tracing Word Relations - An Implicit Crowdsourcing Approach
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource.
Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning
We present our work on the problem of Multiple Admissibility (MA) in language learning.
Grouping business news stories based on salience of named entities
In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles.
