Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RC system for languages without RC training data. Given a target language without RC training data and a pivot language with RC training data (e.g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model. We first translate the data from the target to the pivot language, and then obtain an answer using the RC model in the pivot language. Finally, we recover the corresponding answer in the original language using soft-alignment attention scores from the NMT model. We create evaluation sets of RC data in two non-English languages, namely Japanese and French, to evaluate our method. Experimental results on these datasets show that our method significantly outperforms a back-translation baseline of a state-of-the-art product-level machine translation system.
This paper describes the EQueR-EVALDA Evaluation Campaign, the French evaluation campaign of Question-Answering (QA) systems. The EQueR Evaluation Campaign included two tasks of automatic answer retrieval: the first one was a QA task over a heterogeneous collection of texts mainly newspaper articles, and the second one a specialised one in the Medical field over a corpus of medical texts. In total, seven groups participated in the General task and five groups participated in the Medical task. For the General task, the best system obtained 81.46 of correct answers during the evalaution of the passages, while it obtained 67.24 during the evaluation of the short answers. We describe herein the specifications, the corpora, the evaluation, the phase of judgment of results, the scoring phase and the results for the two different types of evaluation.