Neural Dialogue Generation

Impressed by authors of Adversarial Learning for Neural Dialogue Generation (https://lnkd.in/g3rd3Zf). They have creatively combined Reinforcement Learning, Sequence to Sequence Model and Adversarial Network to generate human like Natural Language Conversations. 

Thoughts on Seq2Sql research paper

Spent this week reading the research paper (https://lnkd.in/gcrqPnR) from Salesforce Einstein team. I was impressed to know that Seq2sql model got 70% accuracy in generating SQL from Natural Language. The authors have exploited structure of the SQL and creatively combined LSTM'S and Reinforcement Learning approaches It would be great to know Linkedin community's thoughts on how we can further increase the accuracy of SQL generation? What are the approaches to extend the problem to generate Join Operations and Multi Step SQL statements. How far do you think, we are from replacing Data Analyst's with smart enterprise Information Virtual Agents?

Catching up with developments in Recommendation Algorithms using Deep Learning

In my quest to identify technology and business gaps for Voicy.AI, I have been spending time to catch up with developments in Recommendation Algorithms using Deep Learning. I started my research reading Recommendations paper from YouTube. Recommendation in general is a two step process consisting of Retrieval and Re-Ranking. The authors have phrased the retrieval as a Multi Class classification instead of reusing Inverted Index scoring mechanisms. I liked the tricks of Negative Sampling and Sub-Linear scoring using hashing techniques to optimize for training and serving in production respectively. I than moved to another important development in the recommendation systems leveraging joint training of Wide And Deep Learning Neural Nets pioneered by Apps team at Google. I was impressed with the observation of authors about how Wide Model is good for Memorization and Deep Model is good for generalization.

I than stumbled upon another research paper from UCL folks. The authors have focused on the retrieval problem of recommendations in the context of journalism. I liked how the authors have used structure of the problem and seperate attention models to construct profiles to predict recommendations. It was impressive to see the big leaps by DL algorithms for Recommendation problem from Collaborative Filtering algorithms few years back.

What is your opinion about the next DL paradigm for recommendations? Any suggestions for more popular research papers in DL based recommendations?