# Contextual Aware Joint Probability Model Towards Question Answering System

Source: [Contextual Aware Joint Probability Model Towards Question Answering System](<https://www.sota2.com/research/paper/68fa65a01e209131cea0b57f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:31:37.819Z

Authors: Liu Yang, Lijing Song

Published: 2019-04-17T07:16:10.000Z

## Abstract

In this paper, we address the question answering challenge with the SQuAD 2.0 dataset. We design a model architecture which leverages BERT's capability of context-aware word embeddings and BiDAF's context interactive exploration mechanism. By integrating these two state-of-the-art architectures, our system tries to extract the contextual word representation at word and character levels, for better comprehension of both question and context and their correlations. We also propose our original joint posterior probability predictor module and its associated loss functions. Our best model so far obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.08109>)

[PDF](<https://arxiv.org/pdf/1904.08109.pdf>)