In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.
However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn.
Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.
Ranked #1 on Phrase Ranking on KP20k
First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors.
Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.
In this paper, we present an empirical property of these representations -- "average" approximates "first principal component".
The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts-MCU, BLE, Sensors and Power.
In this paper, we explore to conduct text classification with extremely weak supervision, i. e., only relying on the surface text of class names.
no code implementations • 10 Sep 2020 • Sarah E. Finch, James D. Finch, Ali Ahmadvand, Ingyu, Choi, Xiangjue Dong, Ruixiang Qi, Harshita Sahijwani, Sergey Volokhin, Zihan Wang, ZiHao Wang, Jinho D. Choi
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.
Answering complex questions involving multiple entities and relations is a challenging task.
Therefore, we manually correct these label mistakes and form a cleaner test set.
Ranked #2 on Named Entity Recognition on CoNLL++ (using extra training data)
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration.