A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question.However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer.Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question.In this study, we present a new multihop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data.In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions.The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions.We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning.Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required.

MOTIVATION: Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools. RESULTS: We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance. AVAILABILITY AND IMPLEMENTATION: Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.