Category Neural machine translation

With all the hype about ChatGPT, I decided to try it out myself, asking it to explain the concept of neural machine translation and a more exotic concept from my particle physics research past. Feel free to compare its explanation of NMT to my earlier blog posts.

The upshot of this “test”: Caveat emptor! If you ask ChatGPT to write an article (or even your homework) for you, you should carefully check the content, or better yet, write it yourself. Because it turns out that ChatGPT is very good at inventing references that don’t exist.

Read More

Recently, claims have surfaced that neural machine translation (NMT) produces the same quality as human translators, that is, several NMT providers claim to have achieved or even exceeded human parity. In this article I want to investigate these claims of human parity. An introduction to neural machine translation can be found here.

Read More

So, now I have your attention with that title, let’s delve into the real, less anthropomorphizing question: Is neural machine translation (NMT) biased? I was inspired to write this blog post after watching the documentary Coded Bias (available on Netflix) and a follow-up panel discussion entitled “Is AI racist?” (AI = artificial intelligence). Obviously, I borrowed the title for this blog post from that panel discussion. I highly recommend watching the aforementioned movie, if you haven’t already.

As a white Western European female, I don’t have first-hand experience with racism, so am therefore not really qualified to write a blog post about NMT and racist bias. However, as a female with two advanced STEM degrees (Science, Technology, Engineering and Mathematics), I do know a thing or two about gender bias. I was once told at the beginning of a physics lecture at university that

a woman’s place is in the kitchen.

This is, in fact, a verbatim quote, but I won’t mention names or other details to protect the guilty. Given that I am the world’s worst cook, I did not heed that “advice.” (How many other people do you know who have managed to explode an oven while trying to bake a cake?)

Read More

If you are in any way connected with the world of translation and interpretation, you have certainly asked yourself at least one of the above questions about neural machine translation (NMT). These questions are by no means easy to answer. If you ask n experts, you’ll likely get n+1 different answers. Let me quote a few experts:

Read More

You can find the slides for my presentation “An Introduction to Neural Machine Translation” at the 59th Annual Conference of the American Translators Association here. The handouts summarizing my presentation at ATA58 on neural networks in general can be found here.

I will be giving another presentation at the upcoming ATA Annual Conference in New Orleans, ATA59, jointly in the SciTech and Language Technology tracks. The presentation will give an introduction to neural machine translation. My talk is preliminarily scheduled for the very last time slot on Saturday before the final keynote. I hope to see you there, despite the late hour!

Abstract

“The end of the human translator,” “nearly indistinguishable from human translation” – these and similar headlines have been used to describe neural machine translation (NMT). Most language specialists have probably asked themselves: How much of that is hype? How far can this approach to machine translation really go? How does it work? The presentation will examine one of the available open source NMT toolkits as an illustrative example to explain the underlying concepts of NMT and sequence-to-sequence models. It will follow in the same spirit as last year’s general introduction to neural networks, which is summarized in the accompanying handouts.

Handouts

I have just uploaded the handout for the presentation onto the ATA server. The material is a slightly updated version of my blog post on neural networks, which summarizes my presentation at ATA58. You can download the handout here.