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László Lukács

dblp:38/418 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 44% Question answering and dialogue systems · 44% Probabilistic and Bayesian machine learning · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems › dialogue generation
dialogue response generation		 0.212016
Smart Reply: Automated Response Suggestion for Email · KDD 2016
Natural language and speech › Language models and text generation
text generation		 0.212016
Smart Reply: Automated Response Suggestion for Email · KDD 2016
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
large-scale inference		 0.112016
Smart Reply: Automated Response Suggestion for Email · KDD 2016

Methods — techniques the papers use, named apart from their topics

sequence-to-sequence generation · 0.2deep learning · 0.2
YearPublicationVenuePosition
2016 Smart Reply: Automated Response Suggestion for Email
abstract
In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning.
Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann 0004, Andrew Tomkins, Balint Miklos, Gregory S. Corrado, László Lukács, Marina Ganea, Vivek Ramavajjala
 KDD8