I am sharing here the yearly paper analysis for 2021, containing statistics about ML and NLP publications from the past year. It has arrived later than in previous years - preparing it this time took quite a bit longer than intended. The new data required some manual cleaning and updating of the pipeline, which meant the analysis got delayed quite a bit. But finally, here it is now. The analysis of the papers is done using a series of automated tools. These processes are not perfect so some noise and errors may occur. Some authors have also recently started releasing their papers in an obfusc ..read more

This is a collection of advice that I give to students doing research projects in NLP/ML/AI. It includes suggestions that I wish I had known when I myself first started, as well as lessons from supervising students in previous years. I would recommend reading this once before starting your project, then again after about a month or two into the project. Different things will seem relevant to you. Implementation and debugging Don’t assume that your code is bug free – check and debug. Deep learning code can be difficult to get right. Make sure the tensors have the right shapes and dimensions. O ..read more

This is a collection of advice that I give to students doing research projects in NLP/ML/AI. It includes suggestions that I wish I had known when I myself first started, as well as lessons from supervising students in previous years. I would recommend reading this once before starting your project, then again after about a month or two into the project. Different things will seem relevant to you. Implementation and debugging Don’t assume that your code is bug free – check and debug. Deep learning code can be difficult to get right. Make sure the tensors have the right shapes and dimensions. O ..read more

I ran my paper analysis pipeline once again in order to get statistics for 2020. It certainly was an unusual year. While ML and NLP conferences again had more publications than ever before, most of them needed to quickly adapt to a new remote format. Each conference took a slightly different approach as everyone was trying to figure out how to make this work. I heard especially positive comments about EMNLP 2020, regarding their smooth organisation and engaging technical solutions. Overall, I think the remote format has its pluses and minuses - while it certainly complicates networking and soc ..read more

It is about time we once again take a look at the publication statistics of the past year. 2019 was another record breaking year in machine learning and NLP research. Nearly all conferences had more attendees and more publications than ever before. For example, NeurIPS had 6,743 submissions and 1,428 accepted papers, which eclipses all the previous iterations. Because the conference sold out so fast last year, the organizers had to implement a randomised lottery for the tickets this time. In this post you will find a number of graphs to illustrate the publication patterns from 2019. I have inc ..read more

My previous post on summarising 57 research papers turned out to be quite useful for people working in this field, so it is about time for a sequel. Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). They cover a wide range of different topics, authors and venues. These are not meant to be reviews showing my subjective opinion, but instead I aim to provide a blunt and concise overview of the core contribution of each publication. Given how many papers ar ..read more

The growth of interest in NLP technology, fuelled largely by investment in AI applications, has been accompanied by unprecedented expansion of the preeminent NLP conferences: ACL, NAACL and EMNLP in particular. Grzegorz Chrupała’s graph shows the rapid growth in submissions to each conference in recent years: Figure 1. Graph of conference submission counts, by Grzegorz Chrupała These trends can also be seen in the blog post by the ACL 2019 Chairs. Meanwhile, acceptance rates have tended to remain constant, meaning that the conferences have grown in line with submissions, e.g. from ACL 2019: Ta ..read more

It is time for another yearly update of the publication statistics in Machine Learning and Natural Language Processing. The field has continued to grow very rapidly, both in number of publications and number of attendees, breaking all sorts of previous records. Perhaps most notably the initial release of NeurIPS conference tickets sold out in 11 minutes and 38 seconds. In this post I will provide some finer-grained statistics on these numbers, showing which authors and organizations are publishing most at specific conferences. This year, I have included the following conferences/journals: ACL ..read more

Staying on top of recent work is an important part of being a good researcher, but this can be quite difficult. Thousands of new papers are published every year at the main ML and NLP conferences, not to mention all the specialised workshops and everything that shows up on ArXiv. Going through all of them, even just to find the papers that you want to read in more depth, can be very time-consuming. In this post, I have summarised 50 papers. After going through a paper, if I had the chance, I would write down a few notes and summarise the work in a couple of sentences. These are not meant as re ..read more

It has been a very productive year for NLP and ML research. Both areas continued to grow, with conferences reaching record numbers of publications. In this post I will break these numbers down a bit more, by individual authors and organisations. The statistics cover the following venues: ACL, EMNLP, NAACL, EACL, COLING, TACL, CL, CoNLL, *Sem+SemEval, NIPS, ICML, ICLR. Compared to last year, I’ve now included ICLR which has grown very rapidly in the last two years and become a highly competitive conference. The analysis is done automatically, by crawling publication information from the confere ..read more