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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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Training Neural Response Selection for Task-Oriented Dialogue Systems
Matthew Henderson,Ivan Vulić,Daniela Gerz,Iñigo Casanueva,Paweł Budzianowski,Sam Coope,Georgios P. Spithourakis,Tsung-Hsien Wen,Nikola Mrkšić,Pei-Hao Su +9 more
TL;DR: A novel method which pretrains the response selection model on large general-domain conversational corpora and fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain is proposed.
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Learning Dense Representations for Entity Retrieval.
Daniel Gillick,Sayali Kulkarni,Larry Lansing,Alessandro Presta,Jason Baldridge,Eugene Ie,Diego Garcia-Olano +6 more
TL;DR: It is shown that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search.
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ConveRT: Efficient and Accurate Conversational Representations from Transformers
TL;DR: The proposed ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train, and promises wider portability and scalability for Conversational AI applications.
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ConveRT: Efficient and Accurate Conversational Representations from Transformers
TL;DR: ConveRT as mentioned in this paper is a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train, and it can be transferred to the intent classification task, yielding strong results across three diverse data sets.
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Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
Muthuraman Chidambaram,Yinfei Yang,Daniel Cer,Steve Yuan,Yun-Hsuan Sung,Brian Strope,Ray Kurzweil +6 more
TL;DR: This paper used a dual-encoder based model trained to maximize the representational similarity between sentence pairs drawn from parallel data to map text written in different languages, but with similar meanings, to nearby embedding space representations.
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Proceedings ArticleDOI
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