Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A7MQK4E7L" target="_blank" >RIV/00216208:11320/22:7MQK4E7L - isvavai.cz</a>
Result on the web
<a href="https://ojs.aaai.org/index.php/AAAI/article/view/21407" target="_blank" >https://ojs.aaai.org/index.php/AAAI/article/view/21407</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v36i10.21407" target="_blank" >10.1609/aaai.v36i10.21407</a>
Alternative languages
Result language
angličtina
Original language name
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
Original language description
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence. While previous work has demonstrated effective syntax-guided MRC models, we propose to adopt the inter-sentence syntactic relations, in addition to the rudimentary intra-sentence relations, to further utilize the syntactic dependencies in the multi-sentence input of the MRC task. In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting dependency trees to form global syntactic relations across sentences. We then propose the ISDG encoder that encodes the global dependency graph, addressing the inter-sentence relations via both one-hop and multi-hop dependency paths explicitly. Experiments on three multilingual MRC datasets (XQuAD, MLQA, TyDiQA-GoldP) show that our encoder that is only trained on English is able to improve the zero-shot performance on all 14 test sets covering 8 languages, with up to 3.8 F1 / 5.2 EM improvement on-average, and 5.2 F1 / 11.2 EM on certain languages. Further analysis shows the improvement can be attributed to the attention on the cross-linguistically consistent syntactic path. Our code is available at https://github.com/lxucs/multilingual-mrc-isdg.
Czech name
—
Czech description
—
Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Proceedings of the AAAI Conference on Artificial Intelligence
ISSN
2374-3468
e-ISSN
2571-0966
Volume of the periodical
36
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
11538-11546
UT code for WoS article
—
EID of the result in the Scopus database
—