We can communicate instantly with anyone in the world, which means quick and automatic translation has nearly become a necessity. Over the past 15 years, services like Google Translate have brought machine translation into our lives, and have improved communication across the globe. But even as machine translation technology gets better, there’s still a lot to overcome before machines can replicate human communication. It’s important to not always rely on machines to understand or create language, as they can be inaccurate in many situations. Read on to learn more about language tools and how they can sometimes be detrimental.
Translation Tools Today
In theory, artificial intelligence translation services are truly remarkable—accessible from anywhere and nearly instantaneous, they should be the ultimate bridge builders and barrier breakers. But their results are more often than not inaccurate or even downright bizarre.
In actual practice, however, AI based translation algorithms have great difficulty parsing complex elements of language like metaphors, figures of speech, and double meanings. They also struggle with context, humor and sarcasm. These vital aspects of language are often lost in translation. And while there are some impressive attempts to conquer this barrier, like the Pure Neural Machine Translation, which attempts to understand context instead of translating individual words, we’re still a long ways from a consumer friendly AI service that can identify a metaphor or get a joke.
What About Sentiment Analysis?
Of course, monitoring your social media presence is important for any business. Listening to what people are saying about your brands and products is just as important as posting engaging and informative content. However, relying on AI based tools to monitor and understand social media conversations can be risky, as the automated analysis you’re getting may be partially or totally inaccurate.
Sentiment analysis is an AI based shortcut of sorts that attempts to identify and summarize the sentiment, either positive or negative, of text data such as social media posts or comments. Theoretically this provides a fast and simple way to monitor the tone of the social media conversation driven by on your company, your brand or your products.
But sentiment analysis is problematic for the same reasons as AI based translation tools, being unable to register figures of speech, idioms, or sarcasm—types of communication that are particularly common on social media.
While it certainly can be fun and interesting to experiment with social media listening tools, they can’t be relied on until they can understand the sentiment behind written language.
Teaching artificial intelligences to communicate effectively and accurately is a priority for a number of high-tech powerhouses, so we can expect to see innovative solutions in the coming years. But we aren’t there yet.