I was very happy to win an INLG Test of Time award for my paper “An Architecture for Data-to-Text Systems”, so I thought I’d write a few comments on it.
Society (and most funding agencies) want to see real-world benefits or “impact” from academic research. Of course not all research will have real-world impact, and impact may take years or decades to appear! I share some thoughts on types of impact, barriers to impact, and my personal experiences.
I’ve come to realise that there is some confusion, especially amongst newcomers to NLP/AI, about when a research paper can be presented at two venues. I try to explain the rules and principles as I understand them.
Like many others, I am trying to do too much in my university academic role. I’m looking for areas where I can “do less” without having a major impact on research and teaching.
When I asked participants what they most liked at the recent INLG conference, people highlighted events and sessions which focused on discussion and interaction, not technical research papers. Perhaps there is a lesson here that conferences should focus more on interaction and community, and not simply be regarded as venues for presenting research papers.
I was surprised to find out that some institutions require PhD students to publish a certain number of papers before they can graduate. This is not my view; my goal as a supervisor is to train students to be good scientists, and rigid publication targets are not appropriate for this goal.
I would like neural NLG researchers to focus on more challenging datasets, and make some suggestions.
Seven papers which I blogged or tweeted about in 2020, covering evaluation, safety, engineering and system building, and long-term perspective on NLP. I recommend these to all; they made an impact on me, perhaps they will make an impact on you as well!
I was shocked when a PhD student recently told me that he thought he had to focus on end-to-end neural approaches, because this dominates the conferences he wants to publish in. I’m all for research in end-to-end neural, but fixating on this to the exclusion of everything else is a mistake. Especially since end-to-end neural approaches do not currently work very well.