5 episódios

Mind Your NLP brings to you short and crisp discussions on Applied NLP. We analyse papers, books, videos and applications in the field of NLP but only from a practical perspective. We do not claim but try to be the mirror to industry's perspective on Natural language Processing.

Mind Your NLP Mind Your NLP

    • Tecnologia

Mind Your NLP brings to you short and crisp discussions on Applied NLP. We analyse papers, books, videos and applications in the field of NLP but only from a practical perspective. We do not claim but try to be the mirror to industry's perspective on Natural language Processing.

    Word2Vec - You shall know a word by the company it keeps

    Word2Vec - You shall know a word by the company it keeps

    In this episode, we talk about Word2Vec
    References -
    CS224n: Natural Language Processing with Deep Learning
    Efficient Estimation of Word Representations in Vector Space
    CBOW, Skip-Gram, Word Vectors, Word Embeddings

    • 7 min
    BERT: The magic model in NLP

    BERT: The magic model in NLP

    In this episode, Abhishek and Shubhi discuss Language Models and how they've evolved from initial stages to the state-of-the-art model BERT.

    Link to BERT : 1810.04805.pdf (arxiv.org)

    Other Resources :
    What is BERT | BERT For Text Classification (analyticsvidhya.com)

    Language Model In NLP | Build Language Model in Python (analyticsvidhya.com)

    Understanding Word Embeddings: From Word2Vec to Count Vectors (analyticsvidhya.com)

    • 13 min
    Question-Answering

    Question-Answering

    In this episode, we talk about what's the intuition behind Question-Answering and also go over our favourite papers in this domain in 2020.
    Links for the paper:

    Unsupervised Question Decomposition for Question Answering
    Fluent Response Generation for Conversational Question Answering

    • 11 min
    Text Summarization

    Text Summarization

    In this episode, we cover the papers we liked the most in ACL 2020 and EMNLP 2020 on the topic of Text Summarisation.

    Link to papers covered here:

    What Have We Achieved on Text Summarization?

    From Arguments to Key Points: Towards Automatic Argument Summarization

    Discourse-Aware Neural Extractive Text Summarization

    Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning

    • 14 min
    Introduction

    Introduction

    Abhishek and Shubhi talk about 2020 and how it's really difficult to find good content on NLP. In this age of information overload, they decide to cut through the clutter and find some gems of NLP.

    • 1m

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