Investigating query expansion and coreference resolution in question answering on BERT
Bhattacharjee, Santanu, Haque, RejwanulORCID: 0000-0003-1680-0099, Maillette de Buy Wenniger, Gideon and Way, AndyORCID: 0000-0001-5736-5930
(2020)
Investigating query expansion and coreference resolution in question answering on BERT.
In: 25th International Conference on Natural Language & Information Systems (NLDB 2020)), 24 - 26 June 2020, Saarbrücken, Germany (Online).
ISBN 978-3-030-51309-2
The Bidirectional Encoder Representations from Transformers (BERT) model produces state-of-the-art results in many question answering (QA) datasets, including the Stanford Question Answering Dataset (SQuAD). This paper presents a query expansion (QE) method that identifies good terms from input questions, extracts synonyms for the good terms using a widely-used language resource, WordNet, and selects the most relevant synonyms from the list of extracted synonyms. The paper also introduces a novel QE method that produces many alternative sequences for a given input question using same-language machine translation (MT). Furthermore, we use a coreference resolution (CR) technique to identify anaphors or cataphors in paragraphs and substitute them with the original referents. We found that the QA system with this simple CR technique significantly outperforms the BERT baseline in a QA task. We also found that our best-performing QA system is the one that applies these three preprocessing methods (two QE and CR methods) together to BERT, which produces an excellent F 1 score (89.8 F1 points) in a QA task. Further, we present a comparative analysis on the performances of the BERT QA models taking a variety of criteria into account, and demonstrate our findings in the answer span prediction task.
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106), European Regional Development Fund, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713567, Science Foundation Ireland (SFI) Grant Number 13/RC/2077
ID Code:
24561
Deposited On:
25 Jun 2020 16:16 by
Rejwanul Haque
. Last Modified 25 Jun 2020 16:16