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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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Investigating Societal Biases in a Poetry Composition System
Emily Sheng,David C. Uthus +1 more
TL;DR: This work introduces a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system and suggests that data augmentation through sentiment style transfer has potential for mitigating societal biases.
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Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation
TL;DR: A supervised data mining method using an accurate early fusion model to improve the training of an efficient late fusion retrieval model and the resulting retrieval model with additional data significantly outperforms retrieval models directly trained with gold annotations.
Patent
Word hash language model
TL;DR: In this article, a language model may be used in a variety of NLP tasks, such as speech recognition, machine translation, sentence completion, part-of-speech tagging, parsing, handwriting recognition, or information retrieval.
Proceedings ArticleDOI
Similarity Metric Method for Binary Basic Blocks of Cross-Instruction Set Architecture
TL;DR: This work proposes a cross-ISA oriented solution for basic block embedding which utilizes an NMT (Neural Machine Translation) model to establish the connection between two ISAs and implements a prototype system MIRROR, which significantly outperforms the representative baseline and can obtain obviously more accurate evaluation results.
Proceedings ArticleDOI
A Technical Question Answering System with Transfer Learning
TL;DR: TransTQA is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past, built upon a siamese ALBERT network, which enables it to respond quickly and accurately.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
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Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
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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.