Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023)

Abstract: Although many large-scale knowledge bases (KBs) claim to contain multilingual information, their support for many non-English languages is often incomplete. This incompleteness gives birth to the task of cross-lingual question answering over knowledge base (xKBQA), which aims to answer questions in languages different from that of the provided KB. One of the major challenges facing xKBQA is the high cost of data annotation, leading to limited resources available for further exploration. Another challenge is mapping KB schemas and natural language expressions in the questions under cross-lingual settings. In this paper, we propose a novel approach for xKBQA in a reading comprehension paradigm. We convert KB subgraphs into passages to narrow the gap between KB schemas and questions, which enables our model to benefit from recent advances in multilingual pre-trained language models (MPLMs) and cross-lingual machine reading comprehension (xMRC). Specifically, we use MPLMs, with considerable knowledge of cross-lingual mappings, for cross-lingual reading comprehension. Existing high-quality xMRC datasets can be further utilized to finetune our model, greatly alleviating the data scarcity issue in xKBQA. Extensive experiments on two xKBQA datasets in 12 languages show that our approach outperforms various baselines and achieves strong few-shot and zero-shot performance. Our dataset and code are released for further research.

## introduction large-scale knowledge bases (kbs) such as freebase @xcite and dbpedia @xcite store huge amounts of structured knowledge. these kbs support a variety of natural language processing tasks, including question answering over knowledge base (kbqa), where models exploit the knowledge related to the questions and precisely identify the answers by reasoning through various kb relations. although most large-scale kbs claim to contain multilingual information, they could not completely support non-english languages as expected. for example, freebase has no translation for the kb relations/attributes in any non-english languages. more than half of the entities in freebase have no chinese translations, despite the fact that chinese is the most spoken non-english language in the world. therefore, these kbs could not directly support question answering in non-english languages, bringing up the problem of answering non-english questions over the kbs constructed in english. in this work, we focus on cross-lingual kbqa (xkbqa), which aims to answer questions over a kb in another language. figure 1 shows a kb subgraph and several factoid questions in non-english languages, which can be answered by a node in the kb subgraph. despite considerable progress in monolingual kbqa, xkbqa receives little attention. a significant challenge in xkbqa is the lack of large-scale xkbqa datasets. such datasets are quite expensive to annotate since the annotators are expected to be multilingual and have background knowledge about kbs. as a result, even the largest xkbqa dataset so far contains only a few hundred questions @xcite . another challenge is that, compared to other cross-lingual tasks, the expression difference between structured kb schemas and natural language questions further hinders the learning of cross-lingual mapping. to address these challenges, we propose to convert the kb subgraphs into natural language texts and leverage the progress in cross-lingual machine reading comprehension (xmrc) to solve the xkbqa task. recently, there has been a series of large-scale xmrc datasets, such as mlqa @xcite , mkqa @xcite and xquad @xcite . multilingual pre-trained language models (mplms), such as mbert @xcite and xlm-r @xcite , achieve competitive performance on these xmrc benchmarks. as for xkbqa, by converting kb subgraphs into natural language texts, we narrow the gap between kb schemes and natural language expressions. we then utilize the plm-based xmrc models finetuned on xmrc datasets to learn the cross-lingual mapping efficiently, even with limited xkbqa annotations. specifically, we first identify the topic entity from the given question, link it to the kb, and extract its n-order neighbors to construct a kb subgraph, following traditional monolingual kbqa methods @xcite . we then convert the subgraph into a question-specific passage with kb-to-text generation models, incorporating the kb triples with contextual expressions. given the converted cross-lingual question-passage pairs, we adopt mplms to rank answer candidates in the passages. as a general framework, our approach can be easily applied to different languages or kbs without specialized modifications. we empirically investigate the effectiveness of our method on two xkbqa datasets, qald-m @xcite and webqsp-zh. qald-m is a collection of a few hundred questions in 11 non-english languages, from a series of xkbqa evaluation campaigns. considering its small size, we also construct a new dataset webqsp-zh with 4,737 chinese questions translated from webqsp @xcite by native speakers. webqsp-zh is much larger in size and involves more natural expressions as the annotators take into account commonsense knowledge and realistic vocabulary choices during manual translation. experimental results demonstrate that our method outperforms a variety of english-as-pivot baselines based on monolingual kbqa models, reaching 74.37% hits@1 on webqsp-zh. moreover, our method achieves strong few-shot and zeroshot performance. using only 10% of the training data, our method performs comparably to several competitive english-as-pivot baselines trained with full training data. for the zero-shot evaluation on qald-m, our method achieves 51.20% hits@1 on average across 11 languages. our main contributions are summarized as: ## methodology we propose a novel approach to tackle xkbqa as reading comprehension. as illustrated in figure 2 , we first convert kb triples into sentences using generation models and obtain question-specific passages for reading comprehension. we then adopt mplms finetuned on xmrc datasets to answer cross-lingual questions according to the converted passages. ## conclusion in this paper, we propose to formulate xkbqa as answering questions by reading passages, benefiting from the recent advance in xmrc. by converting kb subgraphs into passages, we narrow the gap between kb schemas and natural questions under cross-lingual settings. the cross-lingual knowledge in mplms and the rich resources in xmrc alleviate the problem of data scarcity in xkbqa. to facilitate the evaluation of xkbqa, we collect webqsp-zh, a new large-scale xkbqa dataset with more natural expressions. extensive experiments on two datasets with 12 languages show the strong performance of our method under both supervised and zero-shot settings. we hope that our work will inspire more efforts into xkbqa. several promising research directions under our framework include generating better passages for kb subgraphs, supporting more types of kbqa questions, and exploring better el strategies for xkbqa.