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Found 7 papers, 5 papers with code
新型冠状病毒肺炎相关的推特主题与情感研究(Exploring COVID-19-related Twitter Topic Dynamics across Countries)
我们基于从2020年1月22日至2020年4月30日在推特社交平台上抓取的不同国家和地区发布的50万条推文, 研究了有关 2019新型冠状病毒肺炎相关的主题和人们的观点, 发现了不同国家之间推特用户的普遍关切和看法之间存在着异同, 并且对不同议题的情感态度也有所不同。我们发现大部分推文中包含了强烈的情感, 其中表达爱与支持的推文比较普遍。总体来看, 人们的情感随着时间的推移逐渐正向增强。
Attention Guided Dialogue State Tracking with Sparse Supervision
We present results on two public multi-domain DST datasets (MultiWOZ and Schema Guided Dialogue) in both settings i. e. training with turn-level and with sparse supervision.
SemEval-2020 Task 4: Commonsense Validation and Explanation
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons.
Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Introducing common sense to natural language understanding systems has received increasing research attention.
Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
Blame games tend to follow major disruptions, be they financial crises, natural disasters or terrorist attacks.
Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Previous lattice LSTM model takes word embeddings as the lexicon input, we prove that subword encoding can give the comparable performance and has the benefit of not relying on any external segmentor.
Design Challenges and Misconceptions in Neural Sequence Labeling
We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i. e. NER, Chunking, and POS tagging).
