Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction

11/26/2020
by   Yifan Gao, et al.
0

In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find every possible interpretation of the question, and propose a set of disambiguated question-answer pairs. In this paper, we present a model that aggregates and combines evidence from multiple passages to generate question-answer pairs. Particularly, our model reads a large number of passages to find as many interpretations as possible. In addition, we propose a novel round-trip prediction approach to generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output. On the recently introduced AmbigQA open-domain question answering dataset, our model, named Refuel, achieves a new state-of-the-art, outperforming the previous best model by a large margin. We also conduct comprehensive analyses to validate the effectiveness of our proposed round-trip prediction.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/22/2020

AmbigQA: Answering Ambiguous Open-domain Questions

Ambiguity is inherent to open-domain question answering; especially when...
12/25/2019

Learning to Answer Ambiguous Questions with Knowledge Graph

In the task of factoid question answering over knowledge base, many ques...
11/05/2016

Dynamic Coattention Networks For Question Answering

Several deep learning models have been proposed for question answering. ...
11/12/2016

Training IBM Watson using Automatically Generated Question-Answer Pairs

IBM Watson is a cognitive computing system capable of question answering...
01/03/2019

Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

End-to-end neural models have made significant progress in question answ...
10/16/2021

Tackling Multi-Answer Open-Domain Questions via a Recall-then-Verify Framework

Open domain questions are likely to be open-ended and ambiguous, leading...
11/10/2020

Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering

Most approaches to Open-Domain Question Answering consist of a light-wei...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.