Of course you have heard this question. Everybody has! The answer is probably yes. If you even wonder whether you should write something down… do it. Being the person to remind the group of writing results down – or even doing it yourself without even asking that question – might trigger your colleagues to wonder why you insist on this. So let’s quickly review the top three possible reasons.
After I had narrowed down the topic of my first machine learning project to building a movie recommendation algorithm, I quickly found some movie data from the IMDB database that I could use. That way, I could basically skip the whole step of learning how to use an API properly and get the data myself (haha! I thought. Read on).
The next step is to plan out each of your phases which will give you the horizontal level of your workshop plan.
… and what I wouldn’t change. My way to becoming the team leader of multiple teams in the company was
When it comes to measuring quality, we are surprisingly unsuspicious once a metric comes into the play. As soon as someone hands you numbers, or a chart, there is a good chance that you will trust in those numbers – especially if they support what you already believe. It is always important to know where those numbers come from, and what exactly they measure. Especially in the field of (neural) machine translation, trusting numbers blindly can have severe consequences.
In the last part, I talked about why structure matters so much for your workshop. Providing a structure will help your participants to navigate through the endless possibilities that open up to them during discussions and brainstorming phases, and will align all of you to focus on the goal of the workshop. Now, let’s look into different options for structures.
Back in the days when I was a machine translation specialist, it was part of my job to make sure that the machine translation output we used had a certain quality. I was positioned between the Sales and Production departments of the company, because that certain quality was important for both: As the content usually got post-edited, I had to check if the post-editors would actually be able to work with the output. And as machine translation and post-editing (MTPE) was a cheaper product than good old translation, the Sales guys wanted to know how much they could go down with our rates.
Nonviolent communication (NVC) as developed by Marshall Rosenberg in the 1960s is an approach to human interaction based on the assumption that everybody is capable of empathy and compassion, and that conflict only arises when your own needs are not met. NVC is bigger than your workplace – for some, it’s more like a world view, and there are also parenting strategies based on NVC. It basically can be applied to any system or organization, because it’s so universal – that’s the beauty of it!
No, this is not yet another article with motivating mantras about you being good enough. You are! Trust me. This is a blog post about quality assurance. Before I became Head of Technology, my position was Machine Translation Specialist. As such, I was confronted with this question on a daily, nay, hourly basis regarding raw or post-edited machine translation output. I often struggled to answer it, and I could imagine that I am not the only one. So here’s my thoughts – maybe they help you the next time when someone asks you exactly this question.
Planning the structure ahead is essential for your workshop to be successful.
After identifying the topic of my first ML project, I needed to outline my business problem. Following what I had learned in online courses and YouTube videos, I went through these 5 steps.