There were not many papers I made notes about in August (likely because I was on vacation most of it). Anyway, here are three papers that I think should not be forgotten just because they went out in August. A [paper](https://arxiv.org/abs/2208.06061) by folks from JHU, Microsoft and UNC Chappel Hill accepted to [AMTA](https://2022.amta.org) experiment with morphologically rich and rather low-resource languages (Inuktitut and Turkish) and play around with morphological segmentation and additional inductive biases in the Transformer architecture. First, they use morphological segmentation works much better than statistical segmentation. Second, they tried an architecture improvement originally designed for models doing arithmetics, hoping that this component will take over morphological regularities that in some sense remind regularities of arithmetic operations. Folks from NYU and JHU did a [survey among NLP researchers](https://arxiv.org/abs/2208.12852). They asked about what people think about NLP and AI in general and also what they think most of the community thinks. Some of the results are quite interesting. People think big companies have too much influence, two thirds (including me) think that most NLP research is dubious science (but half of the people thinks other do not think so). This discrepancy is even bigger by the question of whether scaling up models will solve virtually all NLP problems, almost no one thinks that, but they think others think so. Microsoft published a pre-print for a [language-vision model called BEIT](https://arxiv.org/abs/2208.10442), which looks like a good model for vision-language modeling. If I were supposed to work on multimodal translation once again, I would definitely have a look at this model. There are no groundbreaking ideas: it is based on masked-language modeling, images are represented with patches, there are specialized heads for modalities... Anyway, it seems to work well, which makes it a valuable resource.