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Efficient Natural Language Response Suggestion for Smart Reply
Matthew L. Henderson,Rami Al-Rfou,Brian Strope,Yun-Hsuan Sung,László Lukács,Ruiqi Guo,Sanjiv Kumar,Balint Miklos,Ray Kurzweil +8 more
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TLDR
A computationally efficient machine-learned method for natural language response suggestion using feed-forward neural networks using n-gram embedding features that achieves the same quality at a small fraction of the computational requirements and latency.Abstract:
This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode messages into vectors which are optimized to give message-response pairs a high dot-product value. An optimized search finds response suggestions. The method is evaluated in a large-scale commercial e-mail application, Inbox by Gmail. Compared to a sequence-to-sequence approach, the new system achieves the same quality at a small fraction of the computational requirements and latency.read more
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AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora
TL;DR: AVScan2Vec as mentioned in this paper is a language model trained to comprehend the semantics of AV scan data and outputs a meaningful vector representation, which can scale to even the largest malware production datasets.
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TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
TL;DR: The authors presented a new unsupervised method based on pre-trained Transformers and sequential denoising auto-encoder (TSDAE) which outperforms previous approaches by up to 6.4 points.
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Language Scaling for Universal Suggested Replies Model
Qianlan Ying,Payal Bajaj,Budhaditya Deb,Yu Yang,Wei Wang,Bojia Lin,Milad Shokouhi,Xia Song,Yang Yang,Daxin Jiang +9 more
TL;DR: This paper proposed a multi-task continual learning framework with auxiliary tasks and language adapters to learn universal language representation across regions, which showed positive cross-lingual transfer across languages while reducing catastrophic forgetting across regions.
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Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning
TL;DR: This article proposed a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms, which achieves better performance over ERM and the existing state-of-the-art models.
Proceedings ArticleDOI
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting.
TL;DR: In this article, a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset is presented.
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Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
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