Papers by Lavinia Aparaschivei

    2 papers
    Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)

    Copied to clipboard

    Challenge: Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones.
    Approach: They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises .
    Outcome: The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs.
    Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet (2020.lrec-1)

    Copied to clipboard

    Challenge: Language resources (LRs) are expensive to create and maintain, and this makes it difficult to create or extend LRs.
    Approach: They propose to use a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet with new words.
    Outcome: The proposed model allows to gather 12,000 answers from learners on different question types over 16 days and shows that it is a potential tool for crowdsourcing and fostering vocabulary skills.

    What is GenGO?

    GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

    Information

    About
    Limitations