Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval
Ivan Montero, Shayne Longpre, Ni Lao, Andrew Frank, Christopher DuBois · 2022
Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English.They not only suffer from the shortcomings of non-English document retrieval, but are reliant on language-specific supervision for either the task or translation.We formulate a task setup more realistic to available resources, that circumvents document retrieval to reliably transfer knowledge from English to lower resource languages.Assuming a strong English question answering model or database, we compare and analyze methods that pivot through English: to map foreign queries to English and then English answers back to target language answers.Within this task setup we propose Reranked Multilingual Maximal Inner Product Search (RM-MIPS), akin to semantic similarity retrieval over the English training set with reranking, which outperforms the strongest baselines by 2.7% on XQuAD and 6.2% on MKQA.Analysis demonstrates the particular efficacy of this strategy over stateof-the-art alternatives in challenging settings: low-resource languages, with extensive distractor data and query distribution misalignment.Circumventing retrieval, our analysis shows this approach offers rapid answer generation to many other languages off-the-shelf, without necessitating additional training data in the target language.