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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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Proceedings Article
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
TL;DR: Cough as discussed by the authors is a large, challenging dataset for COVID-19 FAQ retrieval, which consists of FAQ Bank, Query Bank and relevance set, and is used to test different FAQ retrieval models built on top of BM25 and BERT.
Proceedings ArticleDOI
Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases
TL;DR: A method for training effective language- specific sentence encoders without manually labeled data is proposed, to automatically construct a dataset of paraphrase pairs from sentence-aligned bilingual text corpora and used to tune a Transformer language model with an additional recurrent pooling layer.
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RISE: Leveraging Retrieval Techniques for Summarization Evaluation
David C. Uthus,Jianmo Ni +1 more
TL;DR: This article proposed RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval, which is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries.
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Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation
Teven Le Scao,Claire Gardent +1 more
TL;DR: This article proposed EREDAT (Ensembled Representations for Evaluation of DAta-to-Text), a similarity metric between English text and knowledge base (KB) graphs.
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A Conditional Generative Matching Model for Multi-lingual Reply Suggestion.
TL;DR: This paper proposed Conditional Generative Matching models (CGM), optimized within a variational autoencoder framework, to address challenges arising from multilingual RS and achieved state-of-the-art performance.
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Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
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