Building a Question and Answer System for News Domain
Sandipan Basu, Aravind Gaddala, Pooja Chetan, Garima Tiwari, Narayana, Darapaneni, Sadwik Parvathaneni, Anwesh Reddy Paduri

TL;DR
This paper develops a span-based question-answering system for news articles, utilizing attention mechanisms and transfer learning with BERT, achieving significant improvements over traditional models.
Contribution
The paper introduces a novel attention-based span model for news QA and demonstrates the effectiveness of BERT-based transfer learning in this domain.
Findings
BERT-based model outperforms traditional RNN models in accuracy.
The attention mechanism improves answer span prediction.
Transfer learning significantly enhances model performance.
Abstract
This project attempts to build a Question- Answering system in the News Domain, where Passages will be News articles, and anyone can ask a Question against it. We have built a span-based model using an Attention mechanism, where the model predicts the answer to a question as to the position of the start and end tokens in a paragraph. For training our model, we have used the Stanford Question and Answer (SQuAD 2.0) dataset[1]. To do well on SQuAD 2.0, systems must not only answer questions when possible but also determine when no answer is supported by the paragraph and abstain from answering. Our model architecture comprises three layers- Embedding Layer, RNN Layer, and the Attention Layer. For the Embedding layer, we used GloVe and the Universal Sentence Encoder. For the RNN Layer, we built variations of the RNN Layer including bi-LSTM and Stacked LSTM and we built an Attention Layer…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Linear Warmup With Linear Decay · Sigmoid Activation · WordPiece · Weight Decay · Tanh Activation
