Search Results for author: Matthew Henderson

Found 15 papers, 5 papers with code

Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

no code implementations1 Feb 2022 Joydeep Munshi, Alexander Rakowski, Benjamin H Savitzky, Steven E Zeltmann, Jim Ciston, Matthew Henderson, Shreyas Cholia, Andrew M Minor, Maria KY Chan, Colin Ophus

Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications.

ConVEx: Data-Efficient and Few-Shot Slot Labeling

no code implementations NAACL 2021 Matthew Henderson, Ivan Vulić

We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks.

Language Modelling

Efficient Intent Detection with Dual Sentence Encoders

5 code implementations WS 2020 Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, Ivan Vulić

Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).

Intent Detection Sentence

Training Neural Response Selection for Task-Oriented Dialogue Systems

1 code implementation ACL 2019 Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks.

Chatbot Language Modelling +2

Efficient Natural Language Response Suggestion for Smart Reply

no code implementations1 May 2017 Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil

This paper presents a computationally efficient machine-learned method for natural language response suggestion.

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