Question Answering: Challenges and Solutions

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Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Explores model interpretability in NLP, focusing on probing methods and local explanations to understand model predictions.

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

Introduces the basics of Natural Language Processing, covering challenges, application domains, and linguistic processing levels.

Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.