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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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Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
TL;DR: Automatic and manual evaluation indicates that the proposed novel data augmentation method for training open-domain dialogue models by utilizing unpaired data can produce high-quality dialogue pairs with diverse contents, and can improve the performance of competitive baselines.
Proceedings ArticleDOI
Few-Shot Learning with Siamese Networks and Label Tuning
TL;DR: This work shows that with proper pre-training, Siamese Networks that embed texts and labels offer a competitive alternative in text classification, and introduces label tuning, a simple and computationally efficient approach that allows to adapt the models in a few-shot setup by only changing the label embeddings.
Patent
Removing personal information from text using a neural network
TL;DR: In this paper, a neural network is used to remove personal information from text (such as names, addresses, credit card numbers, or social security numbers), and replace the personal information with a label indicating the type or class of the removed information.
Proceedings Article
SimCSE: Simple Contrastive Learning of Sentence Embeddings
TL;DR: This paper proposed a contrastive learning objective to regularize pre-trained embeddings' anisotropic space to be more uniform, and better aligns positive pairs when supervised signals are available.
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Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models
TL;DR: This paper provided the first exploration of text-to-text transformers (T5) sentence embeddings for language processing tasks and achieved state-of-the-art performance on transfer tasks and semantic textual similarity.
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Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.