Affiliations: University of Roma Tor Vergata, Department of Enterprise Engineering, Rome, Italy
Correspondence:
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Corresponding author: Danilo Croce, University of Roma Tor Vergata, Department of Enterprise Engineering, Rome, Italy. E-mail: [email protected].
Abstract: The recent breakthroughs in the field of deep learning led to state-of-the-art results in several NLP tasks, such as Question Answering (QA). Unfortunately, the requirements of such neural QA systems are very strict due to the size of the involved training datasets. In cross-linguistic settings these requirements are not satisfied as training datasets for QA over non-English texts are often not available. This represents the major barrier for a wide-spread adoption of neural QA methods in NLP applications. In this paper, the acquisition of a large scale dataset for an open-domain factoid question answering system in Italian is discussed. It is obtained by automatic translation and linguistic elicitation of an existing English dataset, i.e. the SQuAD question-answer pair corpus. Even though the quality of the resulting corpus for Italian might not be completely satisfying, our work allowed to generate more than 60 thousand question-answer pairs. In the paper the impact of this resource on the QA process over the Italian Wikipedia is studied, according to different training conditions and architectural constraints. A comparative evaluation against the English version, in line with standards in the SQuAD literature, is carried out. The outcomes show that the results achievable for Italian are below the state-of-the-art for English, but the ability of learning not to respond (i.e. the adoption of techniques for detecting question whose answers are simply not available, i.e. EMPTY set of answers) allows the system to pursue reasonable levels of precision. This make it already usable within realistic application scenarios. Finally, an error analysis is presented that suggests possible future research directions on still critical but highly beneficial enhancements, in view of concrete QA applications in Italian.
Keywords: Question answering in Italian, deep learning, recurrent neural network with
attention
