Multi-domain dialog state tracking using recurrent neural networks a Mrksic et al. 2015 Suppose you want to build a chatbot for a domain in which you don’t have much data yet. What can you do to bootstrap your dialog state tracking system? In today’s paper the authors show how to transfer knowledge across domains, and … Continue reading Multi-domain dialog state tracking using recurrent neural networks
Machine learning for dialog state tracking: a review
Machine learning for dialog state tracking: a review Henderson MLSLP 2015 Today we turn our attention to the task of figuring out, potentially over multiple interactions with a bot, what it is the user is requesting the bot to do. This task goes by the name of Dialog State Tracking, and it’s something that Matthew … Continue reading Machine learning for dialog state tracking: a review
End-to-end learning of semantic role labeling using recurrent neural networks
End-to-end learning of semantic role labeling using recurrent neural networks Zhou & Xu International joint conference on Natural Language Processing, 2015 Collobert’s 2011 paper that we looked at yesterday represented a turning point in NLP in which they achieved state of the art performance on part-of-speech tagging (POS), chunking, and named entity recognition (NER) using … Continue reading End-to-end learning of semantic role labeling using recurrent neural networks
Natural language understanding (almost) from scratch
Natural language understanding (almost) from scratch Collobert et al. Journal of Machine Learning Research 2011 Having spent much of last week looking at non-goal driven dialogue systems trained end-to-end, today it’s time to turn our attention to some of the building blocks of natural language processing that a chatbot can take advantage of if you’re … Continue reading Natural language understanding (almost) from scratch
Building end-to-end dialogue systems using generative hierarchical neural network models
Building end-to-end dialogue systems using generative hierarchical neural network models Serban et al. AAAI 2016 After reading a few of these papers on generative non-goal driven dialogue systems, I’ve ended up both impressed at the early results and the direction they point in, as well as somewhat underwhelmed at the potential for this technology to … Continue reading Building end-to-end dialogue systems using generative hierarchical neural network models
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 neural conversation model
A Neural Conversation Model Vinyals & Le, ICML 2015 What happens if you build a bot that is trained on conversational data, and only conversational data: no programmed understanding of the domain at all, just lots and lots of sample conversations…? Building on the sequence to sequence technique that we looked at previously, this is … Continue reading A neural conversation model
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
the morning paper 