Haibin Liu

Sebastian Ruder: Find a task you’re interested in for instance by browsing the tasks on NLP-progress. If you’re interested in doing research, try to choose a particular subproblem not everyone is working on. For instance, for sentiment analysis, don’t work on movie reviews but conversations. For summarization, summarize biomedical papers rather than news articles. Read papers related to the task and try to understand what the state-of-the-art does. Prefer tasks that have open-source implementations available that you can run. Once you have a good handle of how something works, for research, reflect if you were surprised by any choices in the paper. Try to understand what kind of errors the model makes and if you can think of any information that could be used to mitigate them. Doing error and ablation analyses or using synthetic tasks that gauge if a model captures a certain kind of information are great ways to do this.

g. the fastai com… problems. Particularly in NLP, there are lot of problems with a small number of labelled examples. Write about what you’re doing and learning. Reach out to people with similar interests and focus areas. Engage with the community, e.g. the fastai community is awesome. Get on Twitter. Twitter has a great ML community and you can often get replies from top experts in the field way faster than via email. Find a mentor. If you write to someone for advice, be mindful of their time. Be respectful and try to help others. Be generous with praise and cautious with criticism.