scholarly journals Evaluation and improvement of cross-lingual question answering strategies

2006 ◽  
Author(s):  
Anne-Laure Ligozat ◽  
Brigitte Grau ◽  
Isabelle Robba ◽  
Anne Vilnat
Keyword(s):  
Question Answering ◽  
Cross Lingual

Robust cross-lingual knowledge base question answering via knowledge distillation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shaofei Wang ◽  
Depeng Dang
Keyword(s):  
Knowledge Base ◽  
Design Methodology ◽  
Question Answering ◽  
Ground Truth ◽  
Student Model ◽  
Content Type ◽  
Final Performance ◽  
Knowledge Distillation ◽  
The Cross ◽  
Cross Lingual

PurposePrevious knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.Design/methodology/approachThe authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.FindingsThe experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.Originality/valueThe authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

scholarly journals XQA: A Cross-lingual Open-domain Question Answering Dataset

2019 ◽  
Author(s):  
Jiahua Liu ◽  
Yankai Lin ◽  
Zhiyuan Liu ◽  
Maosong Sun
Keyword(s):  
Question Answering ◽  
Open Domain ◽  
Cross Lingual

scholarly journals Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering

2021 ◽  
Author(s):  
Arij Riabi ◽  
Thomas Scialom ◽  
Rachel Keraron ◽  
Benoît Sagot ◽  
Djamé Seddah ◽  
...  
Keyword(s):  
Question Answering ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Cross Lingual
Sign in / Sign up

Export Citation Format

Share Document