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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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Information Leakage in Embedding Models
TL;DR: The authors demonstrate that embeddings, in addition to encoding generic semantics, often also present a vector that leaks sensitive information about the input data, such as authorship of text, which can be easily extracted by training an inference model on a handful of labeled embedding vectors.
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COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
TL;DR: This work analyzes COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement.
Proceedings ArticleDOI
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
TL;DR: This article proposed a data augmentation method for training open-domain dialogue models by utilizing unpaired data, where both post and response are retrieved from the unpaired training data and a ranking module is employed to filter out low-quality dialogues.
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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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Social media polarization and echo chambers: A case study of COVID-19.
TL;DR: The extent of polarization of COVID-19 discourse on Twitter in the U.S. is studied to provide empirical evidence that political echo chambers are prevalent, exacerbating the exposure to information in line with pre-existing users’ views.
References
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Long short-term memory
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Proceedings ArticleDOI
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Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
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.