[20] t contain the answer, which is then passed to the reader to identify an answer span. To facilitate Anserini RetrieverInvertedIndexQuestiontop ksegmentsAnswerFine-tunedBERT+BERT Readersegment scorespanscore Pretrained BERTIndexingFine-tuning on SQuADFigure 1: Architecture of BERTserini. comparisons to previous work, we use the same Wikipedia corpus described in Chen et al. (2017) (from Dec. 2016) c

 [19] erini represent solid founda- tions on which to build an end-to-end question an- swering system. 3 System Architecture The architecture of BERTserini is shown in Fig- ure 1 and is comprised of two main modules, the Anserini retriever and the BERT reader. The re- triever is responsible for selecting segments of text that contain the answer, which is then passed to the reader to identify an answer s

 [1] End-to-End Open-Domain Question Answering with BERTserini Wei Yang,1;2Yuqing Xie,1;2Aileen Lin,2Xingyu Li,2 Luchen Tan,2Kun Xiong,2Ming Li,1;2andJimmy Lin1;2 1David R. Cheriton School of Computer Science, University of Waterloo 2RSVP.ai Abstract We demonstrate an end-to-end question an- swering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast t

 [27] l., 2017) 29.1 37.5 - Kratzwald and Feuerriegel (2018) 29.8 - - Par. R. (Lee et al., 2018) 28.5 - 83.1 Par. R. + Answer Agg. 28.9 - - Par. R. + Full Agg. 30.2 - - MINIMAL (Min et al., 2018) 34.7 42.5 64.0 BERTserini (Article, k= 5) 19.1 25.9 63.1 BERTserini (Paragraph, k= 29 ) 36.6 44.0 75.0 BERTserini (Sentence, k= 78 ) 34.0 41.0 67.5 BERTserini (Paragraph, k= 100 )38.6 46.1 85.8 Table 1: Results

 [2]  BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answer- ing and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify an- swers from a large corpus of Wikipedia arti- cles in an end-to-end fashion. We report large improvements over previous


Instructions: Compose a comprehensive reply to the query using the search results given. If the search results mention multiple subjects with the same name, create separate answers for each. Only include information found in the results and don't add any additional information. Make sure the answer is correct and don't output false content. If the text does not relate to the query, simply state 'Non è stata trovata una risposta alla tua domanda nel testo'.Ignore outlier search results which has nothing to do with the