Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

03/14/2017
by   Junbei Zhang, et al.
0

The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/31/2019

What Question Answering can Learn from Trivia Nerds

In addition to the traditional task of getting machines to answer questi...
research
05/14/2018

A Cost-Effective Framework for Preference Elicitation and Aggregation

We propose a cost-effective framework for preference elicitation and agg...
research
06/10/2020

ClarQ: A large-scale and diverse dataset for Clarification Question Generation

Question answering and conversational systems are often baffled and need...
research
02/13/2017

Multitask Learning with Deep Neural Networks for Community Question Answering

In this paper, we developed a deep neural network (DNN) that learns to s...
research
09/10/2023

Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities

Community Question Answering (CQA) in different domains is growing at a ...
research
06/04/2018

Neural Network-based exploration of construct validity for Russian version of the 10-item Big Five Inventory

This study aims to present a new method of exploring construct validity ...
research
07/22/2019

Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs

Question answering has emerged as an intuitive way of querying structure...

Please sign up or login with your details

Forgot password? Click here to reset