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Monday, December 1, 2014

Machine Translation Humor Update

It has been sometime since I first wrote a blog post about MT humor primarily because I really have not been able to find anything worth the mention, until now, and except for some really lame examples about how MT mistranslates (sic) I have not seen much to laugh heartily at. It seems a group of people on the web have discovered the humorous possibilities of MT in translating song lyrics which might be difficult even for good human translators. (It really seems strange to be saying “human translator”.) 

I should point out that in all these recent cases one does have to work at degrading the translation quality by running the same text through a whole sequence of preferably not closely related languages.

It has often surprised me that there are some in the MT industry who use “back translation” as a way to check MT quality, as from my vantage point it is an exercise that can only result in proving the obvious. MT back translation by definition should result in deterioration since to a very great extent MT will almost always be something less than a perfect translation. This point seems to evade many who advocate this method of evaluation, so let me clarify with some mathematics as math is one of the few conceptual frameworks available to man where proof is absolute or pretty damned certain at least.

If one has a perfect MT system then the Source and Target segments should be very close if not exactly the same. So mathematically we could state this as:


Source (1) x Target (1) = 1

since in this case we know our MT system is perfect ;-)

But in real life where humans play on the internet and you have DIY MT systems being used to determine what MT can produce, the results are less likely to equal 1 which is perfect as shown in the example above.

So lets say you and I do a somewhat serious evaluation of the output of various MT systems (each language direction should be considered a separate system) and find that the following table is true for our samples by running 5,000 sentences through various MT conversions and scoring each MT translation (conversion) as a percentage “correct” in terms of linguistic accuracy and precision.

Language Combination Percentage Correct
English to Spanish 0.8 or 80%
Spanish to English 0.85 or 85%
English to German 0.7 or 70%
German to English 0.75 or 75%

So if we took 1,000 new sentences and translate them with MT we should expect that the percentage shown above would be “correct” (whatever that means). But if we now chain the results by making the output of one, the input of the other, we will find that results are different and and get continually smaller e.g.

EN > ES > EN = .8 x .85 = 0.68 or 68% correct


EN > DE > EN = .7 x .75 =  0.525 or 52.5% correct

So with MT we should expect that every back test will result in a lower or degraded results as we are multiplying the effect of two different systems. Since computers don’t really speak the language one cannot assume that they have equal knowledge going each way and if you provide a bad source from system A to system B you should expect a bad target as computers like some people, are very literal.

So now if we take our example and run it through multiple iterations we should see a very definite degradation of the output as we can see below.

EN > ES > EN(from MT) > DE > EN = .8 x .85 x .7 x .75 = 0.357 or 35.7%

So if you are trying to make MT look silly you have to run it through multiple iterations to get silly results. It would help further if you chose language combinations like EN to Japanese to Hindi to Arabic as this would cause more rapid degradation to the original English source. Try it and share your results in the comments. 

So here we have a very nicely done example and you should realize it takes great skill for the lead vocalist to mouth the MT words as if they were real lyrics and still maintain melodic and rhythmic integrity so be generous in your appreciation of their efforts.


This video shows very effectively how using multiple languages very quickly can degrade the original source as you can see when they go to 64 languages. Somehow words get lost and really strange.

And here is one from a vlogger who really enjoys the effect of multiple rounds of MT on a songs lyrics. She is a good singer and is able to maintain the basic melody without breaking into a smile so I found it quite enjoyable  and I would not be surprised that some might believe that these were indeed the lyrics of the song. She has a whole collection of recordings and has what I consider are high production values for this kind of stuff.



And she produces wonderful results on this Disney classic "When you paint the colors of your air can" which used to be a favorite of my daughter. I actually think the song from the Little Mermaid is much funnier and was done by just running it only through four iterations in Google Translate, but since I could not embed it here directly I have given the link.



 Here is another person who has decided that 14 iterations is enough to get to generally funny with this or any pop song. I'm not sure how funny this really is since I don't know the original song.



 So it appears that we are going to see a whole class of songs that are re-interpreted by Google Translate and it is possible to get millions of views as MKR has, and probably even make a living doing this.  So here you see one more job created by MT.

So anyway if somebody suggests doing a back test with MT you should know the cards are clearly stacked against the MT monster and the results are pretty close to meaningless. A human assessment of a targeted sample set of sentences is a much better way to understand your MT engine.

Hope you all had a good Thanksgiving vacation and are not feeling compelled to shop too fervently now. 

In this time of strife and distrust in Ferguson it is good to see spontaneous goodwill and instant musical camaraderie between these amateur musicians. 

 



My previous posts on MT humor for those who care are:
Machine and Human Translation Based Humor

Translation Humor & Mocking Machine Translation

Thursday, September 11, 2014

The Translation Market – Is it Really Understood?

I saw some interesting comments to a blog post by Kevin Lossner that I thought would be good to share with the community that reads this blog, as it raised some cogent points I thought. The comments basically talk about a larger more complex translation market than many of us might believe exists based on market research available. I do not claim to have real insight or knowledge of this larger translation market, but I am definitely aware that the largest translation initiatives in the world are generally overlooked by traditional market research e.g the many branches of the US government (DoD, NSA, CIA, FBI, DIA, State and even Commerce), the EU and I expect many of the clandestine “intelligence” operations around the world, especially amongst the G20 governments.

I would also bet that the really big, almost nation-like, Fortune 100 corporates also have captive and hidden translation operations that are buried and invisible within PR, Marketing and Investor Relations somewhere to translate the stuff that really matters or is really secret. (I would not be surprised if the people in these departments did not even know if a localization team exists elsewhere in the corporation.)  If it really matters, why would you ask Lionbridge or SDL (or any other large LSP) to translate it? definitely is something to ponder upon.   Surely it would be more likely to go to internal subject matter experts, or to trusted and elite boutique services that actually understand the subject matter of the material, and can protect the information with the same zeal and protective assurances as those who create it.  Imagine you are an oil company called ABCP and want to make sure that you look less culpable for a major accident caused by management insistence on moving ahead with a risky drilling project. I think the odds are high that the translators chosen to translate critical memos and communications and "put the right spin on it" before it is shown to regulators are going to be different from the ones that work for Lionbridge since it might save a few billion in damages that will have to be paid.

I also generally expect that specialists, i.e. translators with demonstrated subject domain expertise, will have a much brighter future than those who will translate anything that is within arms reach. Specialization means building subject matter expertise, which I think will matter more and more, and I for one would stay away from LSPs who do not specialize or have long-term demonstrated competence in a few select domains.

I find this discussion interesting also because I think that repetitive, low-value, short shelf-life, bulk (high volume) content is eventually going the way of PEMT or even raw MT, but there is a huge world of high value content that is unlikely ever to head that way until we reach the Star Trek Universal Translator levels of quality, which are not expected to be available till the 24th century. I actually think that IPO and many SEC filing documents (10K, Registration documents) and user manuals of any kind including nuclear machinery and medical equipment are fair game for competent and very specialized PEMT initiatives, but I would not use MT for anything that requires linguistic finesse or reading between the lines e.g. wedding vows, great literature, letters to the board/stockholders or poetry. Even in those areas where you have high volume and lots of repetitive and highly similar content, MT can work well only when real expertise is applied, and there is a real and active collaboration with translators and linguists who all want to produce an engine that will reduce future efforts.
 
These are some of the excerpted and unedited (by me) comments made by Kevin Hendzel at the blog post referenced above written in a more visceral style than the more careful elaboration in much greater detail on his own blog. I don’t agree with everything Kevin says about MT, but I think his views are generally based on deeper observations than “MT is crap” and I can appreciate that we have different views on this issue. (Excerpts printed here with his and Kevin Lossner’s permission.)
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From my own viewpoint, it does seem that the localization industry/bulk translation market has long suffered from a “we’re the only game in town” problem. There’s an amusing story about SeaWorld (an aquatic theme park in the US) that goes a long way toward illustrating this exact echo-chamber problem that the localization industry and pure bulk-market providers seem to be perpetually trapped in. Occasionally you’ll see protesters outside SeaWorld holding up signs that declare: “It’s not SeaWorld, it’s PoolWorld.” The corporate entity SeaWorld telling tourists that these tiny, familiar pools constitute “the sea” does not make them the sea. The sea is immensely, incalculably larger and more complex.
The same is true of the translation market. Referring to the tiny pool you are familiar with (low-end bulk localization and translation) as “the sea” (the whole rest of the market) tends to distort one’s sense of the enormity of the sea, the complexity of sea life, not to mention how damaging it can be to trap sea life in unfamiliar and hostile surroundings. There may also be value in dispensing with a couple of misconceptions.
Myth #1: There are two market segments (premium and bulk) that are easily delineated and the premium market is dramatically smaller than the bulk market.
Reality: There’s a very long continuum that encompasses all market segments, with raw bulk free MT at one end and $25,000 tag line translations of 3 words at the other.
It’s far more accurate to characterize the continuum in terms of gradual and consistent gradations of shade rather than in terms of clear differentiating boundary lines. The “premium vs. bulk” dichotomy is a form of shorthand only. That also applies to price and quality, since the correlation between the two is not always linear. The premium sector includes commercial segments that are fiercely guarded and (often) shrouded in secrecy to prevent additional competition. Many of these are boutique translator-owned companies that deliberately fly under the radar of “research” companies like Nonsense Advisory (itself shamelessly in bed with the large companies it purports to “cover,” and stubbornly resistant to acknowledging its own 50-kilometer-wide blind spots) to avoid alerting other companies to their profitable businesses. There is an astonishing amount of money in these premium sectors. Pure translation alone in the high-end expert pharmaceutical, medical device and IP litigation as well as the premium legal, financial and marketing sectors across all languages and in all countries dwarfs the entire global IT localization industry by about two to three orders of magnitude. There are some years where one single IP pharmaceutical litigation case in Japanese-English alone will run into the $10 - $20 million range – about 10 times the “savings” that TAUS preaches are available to localization companies and their end clients that embrace their “translation as a utility” model in localization. That’s one single translation project in one single language pair. And the net profit margins are considerably higher.
Myth 2: Price is the key differentiator between the premium and bulk market.
Reality: While it’s true that the premium market tends to operate at higher prices, the market really operates on a completely different value proposition than does the bulk market. That proposition is that the cost of failure is dramatically higher than the cost of performance.
So in the premium market, the cost of translation errors – liability, regulatory failure, loss of life, damaging publicity or significant loss of prestige – far outweighs the cost of “getting it right.” Paying whatever cost premium for translation that is necessary to PREVENT the cost of failure is viewed as a wise investment.
In the bulk market, those two are reversed. The cost of failure is low, so there is no corresponding push to invest in getting it right. This can be tested by comparison to the dynamics of other industries, too. The cost of failure for a Walmart product is very low – the consumer almost expects the damn thing to break. It’s the same with cheap online localization and “just good enough to understand it” bulk translation. But a fractured fuel pump on a Boeing aircraft in flight has an enormous cost of failure, so several layers of review, ongoing maintenance and testing as well as regulatory enforcement are built around it in an effort to ensure that does not happen, a process which drives up fuel pump manufacturing costs dramatically.  When the failure of an IPO or the collapse of a deal due to a translation-related regulatory failure or when nuclear weapons are improperly dismantled or lost to unknown people – yeah, that’s a very, very high cost of failure. Wallets open up to pay a premium for translation in these cases. Of course, translators who want to play in this market must be Boeing quality, though, not Walmart. (If any serious person considers this view “elitist,” I will contemplate the validity of that charge when that person agrees to fly on Walmart-manufactured jet aircraft that fly without regulatory approval or oversight.) :)
Myth 3: The largest translation company in the world is Lionbridge, crowned once again by Nonsense Advisory.
Reality: It isn’t. It may be the largest localization company that openly shares public financial data in an easy-to-read format and hence is trivially “researched,” but it omits huge operations that just don’t advertise their existence in quite the same way. For example, there are Global Linguist Solutions and L-3 Inc. just in the US alone. Never heard of either, right? GLS won the original US Army contract to support Iraq ops worth about $4.64 billion over five years after L-3 had the original one pre-Iraq. Perhaps more to the point in terms of current size, the U.S. Army recently awarded a huge US Army contract referred to as DLITE valued at $9.7 billion to 5 companies including those two. Those are JUST the U.S. Army contracts. The open, unclassified ones. This does not include all the other U.S. federal open spending on language services for all the other agencies that these same companies along with DynCorp and McNeil and Booz Allen and a dozen others that have never been to an ATA or any other translation conference compete for and win. It also omits all U.S. classified and confidential contracts. It omits all other governments’ outsourced classified and unclassified language spending. It’s like omitting the Indian Ocean and half the Pacific from your "research."
It’s a vast, complex, cloudy and immensely varied translation sea out there.
I know that those who have dealings with the US government around translation technology at least have an inkling that this is true. It is sort of like the discussions on the Deep Web which contains much of the highest value information available in the world that is not indexed or accessible by the search engines that we all use. This is the part that is private, gated and contains the really important high value content that can only be seen by people who are properly authenticated and authorized. I can’t say for certain that the proportions in the graphic below are true for the translation market but based on what I directly know about the data volumes processed in the clandestine communities it certainly would not be impossible.
Deep Web icebergdeepweb
Anyway I thought this subject was interesting and worth more exposure. Also, it was easy to do as Kevin Hendzel wrote the bulk of this post.Smile   

P.S.  I thought it was worth adding this post-script here since Luigi Muzii has also made extended comments on his blog on this subject and so I add his Twitter comment to the main body of this post.

From @ilbarbaro
My comments to @kvashee latest debated post can be found in goo.gl/Zpu6Mh, goo.gl/OfZRsB and goo.gl/bN1HJB

Friday, July 25, 2014

Understanding The Drivers of Success with the Business Use of Machine Translation

We have reached a phase where there is a relatively high level of acceptance of the idea that machine translation can deliver value in professional translation settings. But as we all know the idea and the reality can often be far apart. It would be more accurate to say this acceptance of the idea that MT can be valuable, is limited to a select few among large enterprise users and LSPs (the TAUS community) and has yet to reach the broad translator community who continue to point out fundamental deficiencies in the technology or share negative experiences with MT.   So while we see growth in the number of attempts to use  MT, as it has gotten mechanically easier to do, there is also more evidence that many MT initiatives fail in achieving sustainable efficiencies in terms of real translation production value.

In a typical TEP (Translate-Edit-Proof) business translation scenario, A "good" MT system will provide three things to be considered successful:

1) Faster completion of all future translation projects in the same domain
2) Lower cost/word than doing it without the MT system
3) Better consistency on terminology especially for higher volume projects where many translators need to be involved

All of this should happen with a final translation delivered to the customer that is indistinguishable in terms of quality from a traditional approach where MT is not used at all.


It is useful to take a look at what factors underlie success and failure in the business use setting, and thus I present my (somewhat biased) opinions on this as a long-time observer of this technology (largely from a vendor perspective). I think that to a great extent we can already conclude that MT is very useful to the casual internet user, and we see that millions use it on a regular basis to get the gist of multilingual content they run into while traveling across websites and social platforms. (e.g. I use it regularly in Facebook.)

What are the primary causes of failure with MT deployments in business translation settings?

Incompetence with the technology: The most common reason I see for failed deployments is the lack of understanding that the key users have about how the technology works. Do-it-yourself (DIY) tools that promise that all you need to do is upload some data and press play are plentiful, and often promise instant success. But the upload and pray approach does not often work to provide any real satisfaction and business advantage. Unfortunately the state of the technology is such that some expertise and some knowledge are required. The translators and post-editors who have to work with the output results of these lazy Moses efforts, are expected to clean-up and somehow fix this incompetence usually at lower wage rates. And thus resentment grows and many are speaking up frequently in blogs and professional forums about bad MT experiences. Those that have positive MT experiences rarely speak up in these forums since the work is not so different from regular TM-based translation work and MT is often regarded as just another background tool that helps to get a project done faster and more consistently. MT output that does not provide cost and turnaround advantages for translation work cannot be considered to be useful for any professional use. Thus, a minimum requirement for using MT in professional settings is that it should enhance the production process.

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Lowering cost is the ONLY motivation: The most na├»ve agencies simply assume that using MT, however incompetently, is a way to reduce the cost of getting a translation project done, or more accurately a way to justify paying translators less. Thus the post-editors are often in a situation where they have to clean up low quality MT output for very low wages. Given that we live in a world where the customers who pay for professional translation are asking for more efficient translation production i.e. faster and cheaper, agencies are being forced to explore how to do this, but this exploration needs to happen from a larger vision of the business.  As Brian Solis points out, using technology without collaboration and vision is unlikely to succeed (emphasis mine).
"That's the irony about digital transformation, it doesn't work when in of itself technology is the solution. Technology has to be an enabler and that enabler needs to be aligned with a bigger mission. We already found that companies that lead digital transformation from a more human center actually bring people together in the organization faster and with greater results," Solis says. “When technology is heralded above all else, there becomes an even greater disconnect between employees (translators)  and the challenges that their business is trying to solve.”
What many LSPs fail to understand is that their customers are asking for ongoing efficiencies, and new production models to handle the new kinds of translation challenges they face in their businesses. They are not just asking for a lower rate for a single project. Agencies focused on the bigger picture are asking questions like how MT can enable them to achieve new things and what's different about their customers needs today versus yesterday. With the right MT strategy in place, technology becomes an enabler, not the answer and enables agencies to build strong long-term relationships with customers who could not get the same price/performance with another agency that does not understand how to leverage technology for these new translation challenges. Agencies must evolve and reimagine their internal process, structure and culture to match this evolution in customer behavior among their own employees and translators.

No engagement with key stakeholders: Many if not all the bad MT experiences I hear about have one thing in common. Very poor communication between the MT engine developers (LSP), the customer and the translators and editors. MT is as much about new collaboration models as it is about effective engine development, and collaboration cannot happen without open and transparent communication, especially during the initial learning phase when there is a great deal of uncertainty for all concerned. If this communication process is in place in the early projects, it enables everybody to rise together in efficiency, and gets easier and more streamlined and more accurately predictable with each successive MT project. The communication issue is quite fundamental and I have tried to address and explore this in a previous post


What are the key drivers of successful deployments of MT?

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Expert MT Engine Development: The building of MT engines has gotten progressively easier in terms of raw mechanics, but the development of MT engines that provide long-term competitive advantage remains a matter of deep expertise and experience. If as an LSP, you instantly create an MT engine that any of your competitors could duplicate with little trouble, you have achieved very little. Developing MT systems that provide long-term production advantage and a real competitive advantage is difficult, and requires real expertise and experience. The odds of a developer who has built thousands of engines producing a competitive engine are much higher than someone who uploads some data and hopes for the best. Skillful MT engine development is an iterative process where problems are identified and resolved in very structured development cycles so that the engine can improve continuously with small amounts of corrective feedback. Knowing which levers to pull and adjust to solve different kinds of problems is critical to developing competition beating systems. Really good systems that are refined over time are very difficult to match and will continue to provide price/performance advantages over the long-term that competitors will find difficult to match.

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Engaged Project Managers and Key Translators: The most valuable feedback to enhance MT system output will come from engaged PMs and translators who see broad error patterns and and can help develop corrective strategies for these errors. Executives should always strive to ensure that these key people are empowered and encourage them to provide feedback in the engine development process. For most PMs today, MT is new and an unknown and unpredictable element in the translation production process. Thus in initial projects, executives should allow PMs great leeway to develop critical skills necessary to understand and steer both the translators and the MT engine developers. These new skills are very key to success and can help build formidable barriers to competition. While very large amounts of high quality data can sometimes produce excellent MT systems, a scenario where you have a a good project manager steering the MT developers and coordinating with translators to ensure that key elements of an upcoming project are well understood, will almost always result in favorable results other things being equal, especially with challenging situations like very sparse data or when dealing with tough language combinations. 

Communication and collaboration are key to both short and long-term success. The worst MT experiences often tend to be with those LSPs (often the largest ones ) where communication is stilted, disjointed and focused on CYA scenarios rather than getting the job done right. Successful outcomes are highly likely when you combine informed executive sponsorship, expert MT engine development and have empowered PMs who communicate openly and frequently with key translators to ensure that the job characteristics are well understood and that outcomes have a high win-win potentiality. Even really good MT output can fail when the human factors are not in sync. Remember that some translators really don’t want to do this kind of work and forcing them to do it is in nobody’s interest.

Fair & Reasonable Compensation for Post-Editors: I have noted that a blog post I wrote on this issue almost 30 months ago still continues to be amongst the most popular posts I have written. This is an important issue that needs to be properly addressed with a basic guiding principal, pay should be related to the specific difficulty of the work and quality of the output. So low quality output should pay higher per word rates than very high quality output. This means that you have to properly understand how good or bad the output is in as specific and accurate terms as possible since people’s livelihoods are at stake. This accuracy can be gauged in terms of average expected throughput i.e. words per hour or words per day. You may have to experiment at first and be prepared to overpay rather than underpay. Make sure that translators are involved in the rate setting process and that the rate setting process is clearly communicated so that it is trusted rather than resisted. Translators should also ask for samples to determine when a job is worthwhile or not. The worst scenario is where an arbitrary low rate is set without regard for the output quality, and typically in these scenarios incompetent MT practitioners always tend to go too low on the rates, resulting in discontent all around. 

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Real Collaboration & Trust Between Stakeholders: This may be the most critical requirement of all as I have seen really excellent MT systems fail when this was missing. Translation is a business that requires lots of interaction between humans with different goals and if these goals are really out of sync with each other it is not possible to achieve success from multiple perspectives. Thus we often see translators feel they are being exploited or agencies feeling they are being squeezed to offer lower rates because an enterprise customer has whipped together some second rate MT system together with lots of noisy data for them to “post-edit”. When the technology is used (actually misused) in this way it can only result in a state of in equilibrium that will try to correct itself or make a lot of noise trying to find balance. This I think is the reason why so many translators protest MT and post-editing work. There are simply too many cases of bad MT systems combined with low rates and thus I have tried to point out how a translator can make an assessment of a post-editing job that is worth doing from an economic perspective at least. 

Perhaps what we are witnessing at this stage of the technology adoption cycle is akin to growing pains, like the clumsy first steps of a baby or the shyster attempts of some agencies to exploit translators as some translators have characterized it. Both cases are true I feel. And so I repeat what I said before about building trusted networks as this seems to be an essential element for success.

The most successful translators and LSPs all seem to be able to build “high trust professional networks”, and I suspect that this will be the way forward i.e. collaboration between Enterprises, MT developers, LSPs and translators who trust each other. Actually quite simple but not so common in the professional translation industry.

There seems no way to discuss the use of MT in professional settings without raising the ire of at least a few translators as you can see from some of the comments below. So I thought it might be worth trying to lighten the general mood of these discussions with music. I chose this song carefully as some might even say the lyrics are quite possibly the result of machine translation or not so different from what MT produces. As far as I know it is just one example of the poetic mind of Bob Dylan. If you can explain the lyrics shown below you are a better interpreter and translator than I am. Musically this is what I would call a great performance and a good vibe. So here you have a rendition of Dylan's My Back Pages on the Empty Pages blog.




Crimson flames tied through my ears
Rollin’ high and mighty traps
Pounced with fire on flaming roads
Using ideas as my maps
“We’ll meet on edges, soon,” said I
Proud ’neath heated brow
Ah, but I was so much older then
I’m younger than that now

Half-wracked prejudice leaped forth
“Rip down all hate,” I screamed
Lies that life is black and white
Spoke from my skull. I dreamed
Romantic facts of musketeers
Foundationed deep, somehow
Ah, but I was so much older then
I’m younger than that now

Girls’ faces formed the forward path
From phony jealousy
To memorizing politics
Of ancient history
Flung down by corpse evangelists
Unthought of, though, somehow
Ah, but I was so much older then
I’m younger than that now

A self-ordained professor’s tongue
Too serious to fool
Spouted out that liberty
Is just equality in school
“Equality,” I spoke the word
As if a wedding vow
Ah, but I was so much older then
I’m younger than that now

In a soldier’s stance, I aimed my hand
At the mongrel dogs who teach
Fearing not that I’d become my enemy
In the instant that I preach
My pathway led by confusion boats
Mutiny from stern to bow
Ah, but I was so much older then
I’m younger than that now

Yes, my guard stood hard when abstract threats
Too noble to neglect
Deceived me into thinking
I had something to protect
Good and bad, I define these terms
Quite clear, no doubt, somehow
Ah, but I was so much older then
I’m younger than that now