منابع مشابه
Question Answering on SQuAD
In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention...
متن کاملSQuAD Reading Comprehension
One important task in Natural Language Understanding is Reading Comprehension. Given a piece of text, we want to be able to answer any relevant questions. Using Stanford Question Answering Dataset(SQuAD), which is a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, we built a reading comprehension model that attains 75.2% F1...
متن کاملGoverning the Mod Squad
Computer games have increasingly been the focus of user-led innovations in form of games mods. This paper examines how different kinds of socio-technical affordances serve to organize and govern the actions of the people who develop and share their game mods. The affordances examined include customization and tailoring mechanisms, software and content copyright licenses, game software infrastru...
متن کاملBandwidth of Firing Squad Algorithms
The Firing Squad Synchronization Problem (FSSP) is one of the best-studied problems for Cellular Automata (CA) and is part of many more sophisticated algorithms. Solutions for it such as the one from Mazoyer or Gerken use travelling signals, a basic technique of CA algorithms. Hence in addition to their importance, they represent typical CA algorithms. Beside the interest in their bandwidth fol...
متن کاملReading Comprehension with SQuAD
The SQuAD dataset provides a reading comprehension task, with (question, answer, context) pairs. This paper attempts to develop a model to predict the answer for a given question, context pair. In a model inspired by ”Multi-Perspective Context Matching for Machine Comprehension” by Wang et al, this paper achieves 0.47 F1 score on a validation set.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Astronomy & Astrophysics
سال: 2019
ISSN: 0004-6361,1432-0746
DOI: 10.1051/0004-6361/201936006