Incorporating (a) copying mechanism in sequence to sequence learning

Incorporating copying mechanism in sequence to sequence learning Gu et al. 2016, with a side-helping of Neural machine translation by jointly learning to align and translate Bahdanau et al. ICLR 2015 Today’s paper shows how the sequence-to-sequence conversational model we looked at yesterday can be made to seem more natural by including a “copying mechanism” … Continue reading Incorporating (a) copying mechanism in sequence to sequence learning

A survey of available corpora for building data-driven dialogue systems

A survey of available corpora for building data-driven dialogue systems Serban et al. 2015 Bear with me, it’s more interesting than it sounds :). Yes, this (46-page) paper does include a catalogue of data sets with dialogues from different domains, but it also includes a high level survey of techniques that are used in building … Continue reading A survey of available corpora for building data-driven dialogue systems

On chatbots

No paper today, instead a short piece to tee-up the next mini-series of papers I'll be covering... There’s a lot of excitement around chatbots in the startup community. You can divide this into two broad classes: Consumer-oriented services that want to reach an audience which increasingly spends most of its time in messaging applications. Here … Continue reading On chatbots

Transactional data structure libraries

Transactional Data Structure Libraries Spiegelman et al. PLDI 2016 Today’s choice won a distinguished paper award at the recent PLDI 2016 conference. Spiegelman et al. show how to add transactional support to in-memory concurrent data structure libraries in a way that doesn’t sacrifice performance. Since the advent of the multi-core revolution, many efforts have been … Continue reading Transactional data structure libraries