Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Question Answering | Summary and Q&A

TL;DR

Question Answering and Reading Comprehension models, such as LSTM-based models and BERT, have revolutionized the field, achieving high performance on standard datasets like SQuAD. However, adversarial examples and out-of-domain distributions still pose challenges.

Key Insights

• ⚾ QA/RC models have made significant advancements with the introduction of LSTM-based models and BERT.
• 🫠 Pre-training objectives play a crucial role in the performance of reading comprehension models, and improvements can be made to enhance their capabilities.

Transcript

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Questions & Answers

Q: What are some challenges that QA/RC models face?

QA/RC models face challenges such as adversarial examples that can trick the models and out-of-domain distributions that are different from the training data, leading to decreased performance.

Q: What are some popular QA/RC datasets?

Some popular QA/RC datasets include SQuAD, Stanford Question Answering Dataset, which consists of annotated passages and question-answer pairs, and MS MARCO, which focuses on document ranking and passage ranking tasks.

Q: What are some key differences between LSTM-based models and BERT?

LSTM-based models rely on recurrent layers to capture sequential information, while BERT utilizes the transformer architecture, which is parallelizable and allows for more efficient training. BERT's pre-training on large amounts of text has been shown to be highly effective.

Q: How can pre-training objectives be improved for reading comprehension?

One approach is to consider contiguous spans as pre-training objectives, mimicking the target answer span in QA/RC tasks. Another approach is to focus on predicting the start and end positions of the answer span, compressing all the necessary information into these two points.

Summary & Key Takeaways

• Question Answering and Reading Comprehension (QA/RC) models have made significant progress in recent years, driven by deep learning techniques.

• Models like LSTM-based models and BERT have demonstrated high performance on standard datasets, such as SQuAD.

• However, these models still struggle with adversarial examples and out-of-domain distributions, which can lead to decreased performance.