A Cross-Layer Connection Based Approach for Cross-Lingual Open Question Answering

Cross-lingual open domain question answering (Open-QA) has become an increasingly important topic. When training a monolingual model, it is often necessary to use a large number of labeled data for supervised training, which makes it difficult to real applications, especially for low-resource languages. Recently, thanks to multilingual BERT model, a new task, so called zero-shot cross-lingual QA has emerged in this field, i.e., training a model for a language rich in resources and directly testing in other languages. The existing problems in the current research include two main points. The one is in document retrieval stage, directly working multilingual pretraining model for similarity calculation will result in insufficient retrieval accuracy. The other is in the stage of answer extraction, the answers will involve different levels of abstraction related to retrieved documents, which needs deep exploration. This paper puts forward a cross-layer connection based approach for cross-lingual Open-QA. It consists of Match-Retrieval module and Connection-Extraction module. The matching network in the retrieval module makes heuristic adjustment and expansion on the learned features to improve the retrieval quality. In the answer extraction module, the reuse of deep semantic features is realized at the network structure level through cross-layer connection. Experimental results on a public cross-lingual Open-QA dataset show the superiority of our proposed approach over the state-of-the-art methods.