Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval

• URL: http://arxiv.org/abs/2012.14094v1
• Date: Mon, 28 Dec 2020 04:38:45 GMT
• Title: Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval
• Authors: Ivan Montero, Shayne Longpre, Ni Lao, Andrew J. Frank, Christopher DuBois
• Abstract summary: Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English. We formulate a task setup more realistic to available resources, that circumvents document retrieval to reliably transfer knowledge from English to lower resource languages. Within this task setup we propose Reranked Maximal Inner Product Search (RM-MIPS), akin to semantic similarity retrieval over the English training set with reranking.
• Score: 4.4973334555746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 state-of-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 almost any language off-the-shelf, without the need for any additional training data in the target language.

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