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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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Adam: Dense Retrieval Distillation with Adaptive Dark Examples
TL;DR: ADAM as discussed by the authors proposes a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with Adaptive Dark exAMples, which creates dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity using Contrastive Learning and Structured Knowledge
TL;DR: This article proposed AspectCSE, an approach for aspect-based contrastive learning of sentence embeddings, which achieves an average improvement of 3.97% on information retrieval tasks across multiple aspects compared to the previous best results.
Journal ArticleDOI
Generating multiple-choice questions for medical question answering with distractors and cue-masking
TL;DR: The authors showed that fine-tuning on generated MCQA dataset outperforms the masked language modeling based objective and correctly masking the cues to the answers is critical for good performance.
Journal ArticleDOI
CLeBPI: Contrastive Learning for Bug Priority Inference
TL;DR: Zhang et al. as discussed by the authors proposed a contrastive learning for bug priority inference (CLeBPI) which leverages pre-trained language model to learn contextual representations of bug reports without manual feature engineering.
Proceedings Article
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
TL;DR: This paper proposed DialogueCSE, a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues, which generates a context-aware embedding for each candidate response embedding according to the guidance of the multi-turn context-response matching matrices.
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Sequence to Sequence Learning with Neural Networks
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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.