Lexical versus interpretive fidelity--DRAFT

Machine translations of English to Czech have proven disappointing, not that this isn't a problem with other language pairs.

Even though Czech speakers depend upon DeepL and (very few) other translation services, they can judge the quality of machine translation. However, isn't it true that, for the most part, once meaning has become clear with the aid of the Czech rendered, albeit not perfect, that is enough? The meaning-maker can and arguably does move on once that goal is reached, and then discards and proceeds with life beyond the source and translated texts.

If the purpose on the other hand is not just expedient meaning-making but accuracy of expression to be compared favorably with the original (that is, the source), and that translation will be shared (e.g., published) with a critical and sensitive audience of whatever age, background, etc., human intervention is required--perhaps mandatory. Authors would no doubt demand the best translations of their work, wouldn't they? Don't they?

AI and machine translations, it is said, have not been able to replicate the fully human side of communication. Implementations fail at the requisite interpretative expertise of qualified language professionals.

Recently, DeepL failed to translate to translate a  children's story properly from the original English. (You would think that this would be a straight-forward, simple and correctly executed task.) Two problems Czech proofreaders pointed out: One, the Czech translation followed the sentence structure of the English; therefore not authentic as Czech is spoken/used. Two, it failed to nest the story in "the „colours" of the native language intended for a children's book."  

(In another post here, literature's challenge to AI to reach or match the original composition has been pointed out by professional translators in The Netherlands and elsewhere.) 

This then becomes the next question: Can one prompt a better-than-average AI service to do its best to achieve what basic translation fails to achieve per the native speaker? if, that is, one can articulate what it is that the service needs to focus on? Or in the words of one children's book author, "are we close to somehow matching human creativity?"

Now along comes ChatGPT with this prompt.

How can I prompt you to translate something from one language to another and keep what the novelist, for example, crafted? That is, I assume literature is harder to translate than, say, a business letter or legal contract where literal meaning is paramount. A novel, for example, will have allusions, well-turned phrases, and so on.

[begin response]]

You’re exactly right: literary translation is a *different task* from informational translation. The key is to prompt for **interpretive fidelity** rather than **lexical fidelity**.

Below is a practical way to think about prompting, followed by concrete prompt templates you can reuse.

---

## 1. What you’re really asking for

A good literary translation tries to preserve several layers at once:

1. **Meaning** (what happens, what is said)
2. **Voice** (tone, register, narrator’s personality)
3. **Effect** (what the passage does to the reader)
4. **Craft choices** (rhythm, imagery, metaphor, ambiguity)
5. **Cultural signals** (allusions, idioms, social cues)

A literal translation prioritizes #1.
A literary translation tries to balance all five.

Your prompt should *explicitly say* that this is the goal.

---

## 2. The single most important instruction

> **Tell me to act as a literary translator, not a literal one.**

That one sentence changes everything.

Bad prompt:

> “Translate this from Spanish to English.”

Much better:

> “Translate this as literary prose, preserving voice, imagery, and emotional effect rather than word-for-word accuracy.”

---

## 3. A strong general-purpose literary translation prompt

You can reuse this almost verbatim:

> **Prompt**
>
> “Translate the following text from **[Language A]** into **[Language B]** as a *literary translation*.
>
> Priorities:
>
> * Preserve the author’s voice, tone, and rhythm
> * Recreate imagery and metaphor naturally in the target language
> * Favor equivalent expressions over literal phrasing
> * Maintain ambiguity and subtext where present
>
> Avoid stiff or overly literal language. The result should read like an original literary work in **[Language B]**.”

This tells me:

* *What kind* of translation you want
* *What to sacrifice* if tradeoffs are necessary

---

## 4. When you want even more control

### A. Ask for register and style matching

> “The original is understated, ironic, and slightly archaic. Reflect that in the translation.”

or

> “The prose is spare and rhythmic, in the style of early Hemingway.”

This helps preserve *feel*, not just content.

---

### B. Ask me to explain difficult choices (optional)

This is extremely useful for literary work:

> “After the translation, briefly explain any major interpretive choices or places where a literal translation was avoided.”

This keeps you in control as an editor or adaptor.

---

### C. Ask for multiple variants (power move)

Professional translators do this.

> “Provide two translations:
>
> 1. A close literary translation
> 2. A freer, more idiomatic version
>    Briefly note the differences.”

This lets you choose phrasing the way a novelist would.

---

## 5. Handling allusions, wordplay, and untranslatables

You can explicitly instruct how to treat them:

> “If the text contains wordplay or cultural references that don’t transfer cleanly, adapt them creatively rather than explaining them.”

—or—

> “Preserve the original reference even if it feels foreign; do not domesticate it.”

Those are *opposite* philosophies, and translators debate them endlessly. Your prompt decides.

---

## 6. If the author matters

If the text is clearly in dialogue with a known tradition or author:

> “Translate this as if the author were writing originally in **[target language]**, but without modernizing the voice.”

or

> “This is 19th-century prose; avoid contemporary idioms.”

---

## 7. A minimal but effective version

If you want something short you can type quickly:

> “Translate this literarily, not literally—preserve voice, imagery, and emotional effect.”

That alone already works surprisingly well.

---

## 8. One honest limitation (important)

Even the best literary translation is **an interpretation**. There is no neutral or “correct” version—only defensible choices. The more guidance you give about tone, era, and priorities, the closer the result will be to what *you* value in the original.

If you like, you can paste a short literary passage (even a paragraph), and I can:

* show how different prompts change the outcome, or
* translate it and annotate the craft decisions line by line.

That’s often the most illuminating way to see this in action.

[end response]

Given this comprehensive response and guidance, and had the children's story author and colleagues alluded to above known and used this, would the machine translation have (almost) met the experts assessment for a final copy?

That is the next project, which will still (opinion!) require competent language users to sign off on final versions. 

Here is the agenda for the next developments in inter-linear translation conversion tools.

Convert side-by-side (parallel) translation display to inter-linear

You have source text and translation and you are looking at a side-by-side (parallel) display but would prefer, for whatever reason, to have an interlinear-like display.

One alternative is to prompt a chatbot (AI service) to make the translation and display source and target the way you want. 

Another is to take the text and translation and pop them into this tool for an inter-linear display.

  

See if it works for you. It can be used online and off-line. 

Fable Cottage

See https://inter-linear.blogspot.com/2025/12/fable-cottage-subtitled-text-online.html for the link to an online subtitled text readings site. Well worth a look, and hang out for a while to experience all their free stuff, if not subscribe. Great application of interlinear-like reading, learning, etc.
jkm

Interlinearizer

Online application to interlinearize text.

 

 

1st contact, conversation online

[Boilerplate for future online conversation
Boilerplate pro budoucí online konverzaci

partner.
partner.]

We can try online chat. Here is how it works.
Můžeme zkusit online chat. Zde je návod, jak to funguje.

You decide when you can meet online with me. Tell
Vy rozhodujete, kdy se se mnou můžete setkat online. Sdělit

me date and time. If I am available, I will tell
mě datum a čas. Pokud budu k dispozici, řeknu

you. I do not have meetings before 10 in the
Vy. Nemám schůzky před 10

morning.
ráno.

Five or ten minutes before the scheduled meeting,
Pět nebo deset minut před plánovanou schůzkou,

I will send you an email with a link.
Pošlu vám e-mail s odkazem.

You click on the link and enter the Google Meet
Kliknete na odkaz a vstoupíte do Google Meet

space. There we can see and talk with each other.
plocha. Tam se můžeme vidět a mluvit spolu.

While conversing, I make chat notes. After we say
Během konverzace si dělám poznámky do chatu. Poté, co řekneme

good-bye, I clean up the notes and send a copy
sbohem, uklidím poznámky a posílám kopii

for you to study or review.
abyste si je mohli prostudovat nebo prohlédnout.

These meetings are conversations, not classroom
Tato setkání jsou rozhovory, nikoli třída

lessons. So we talk about what you want to talk
lekce. Takže mluvíme o tom, o čem chcete mluvit

about. I ask questions. You ask questions. We
o. kladu otázky. Kladete otázky. My

tell lies. It goes like that till the end. It is
lhát. Tak to jde až do konce. To je

a 45 minute meeting. The first is always free.
schůzka na 45 minut. První je vždy zdarma.

When do you want to meet?
Kdy se chceš sejít?


==================================================
Display Order: Source first
Splitting: Character
Service: Google
Source Characters: 806
Translated Characters: 767
Source Words: 160
Translated Words: 134
Generated: 18 November 2025

Best practices text segmentation

Best practices for text segmentation in translation include using Computer-Assisted Translation (CAT) tools to break text into meaningful, logical units like sentences or phrases, ensuring segments are concise and retain complete units of meaning to fit short-term memory, and prioritizing consistency through defined rules and Translation Memories (TMs) to improve quality and efficiency. Additionally, proper source document formatting, including clear structure and avoiding unnecessary formatting, helps CAT tools parse content correctly and create clearer segments. 

Understanding Text Segmentation

• Definition: Text segmentation is the process of dividing a source text into smaller, translatable units, called "segments". 

Best Practices for Text Segmentation

1. Break into meaningful units: Segment text at natural linguistic boundaries, such as sentences, rather than arbitrary points like every 5-10 words.
2. Keep segments concise: Segments should be short enough for a translator to easily retain the information in their short-term memory.
3. Ensure completeness: Each segment should represent a complete thought or unit of meaning to avoid confusion and an unnatural translation.
4. Utilize CAT Tools & Translation Memories: CAT tools, when configured with appropriate segmentation rules, help identify and manage these units. This promotes consistency and allows for the reuse of previously translated segments in a Translation Memory.
5. Format source content well: Clear, well-organized source documents with consistent formatting (e.g., proper use of paragraph breaks, hard returns, and page breaks) ensure that CAT tools can parse the content correctly and produce unambiguous segments.
6. Define segmentation rules: Establish clear rules, often in a format like SRX (Segmentation Rules Exchange), to define how text should be broken down for specific projects or language pairs.
7. Perform automated QA checks: After segmentation and translation, automated quality assurance checks help identify and correct errors like misspellings or incorrect terminology, preventing them from being added to the Translation Memory.
8. Prioritize consistency: Over time, consistent segmentation practices, combined with well-maintained TMs, significantly increase content reuse, reduce translation costs, and boost overall translation quality. 

• Purpose: It makes translation faster, easier, and more consistent by allowing translators to focus on smaller, logical chunks of text. 
• Tools: Segmentation is a foundational step in Computer-Assisted Translation (CAT) tools and is configured using specific segmentation rules. 

New kid on the block

 Try inter-linear.com. 

Enter text or upload a file, select a translation service, submit this source for translation and display to copy or download. The display, in contrast to side-by-side or parallel texts, can be segmented by sentences, paragraphs, word/phrase, or characters per line.

Additionally there are tools for converting source and translation into inter-linear displays as well as segmenting text into sentences.

Expect output suitable for everyday languages enthusiasts, not for scholarly or biblical exegesis.

Provide feedback on your experience with inter-linear displays and the results you got or wanted, or just enjoy the possibilities.

 

Best strategy? for AI inter-linea display

 The best strategy for producing an interlinear display of a translation using Google Translate is to translate the complete submission first and then use client-side logic (JavaScript/CSS) for display conversion.

---

Analysis of Strategies

1. Translate Complete Submission First (Recommended)

   • Process: Send the full sentence or segment to the Google Cloud Translation API. The API uses Neural Machine Translation (NMT), which translates the entire sentence at once to ensure contextual accuracy and capture nuances, idiomatic expressions, and complex grammar.¹ The complete translated text is returned. Client-side code (JS/CSS) is then used to align the original text with the translated text for an interlinear (line-by-line or word-under-word) display, often by segmenting the text into smaller chunks (e.g., words, phrases) and attempting to map them, or simply displaying the source text and the full translation on separate lines.

   • Pros:

     • Highest Quality Translation: NMT produces the most human-like and contextually accurate result.²

     • Efficiency: Fewer API calls are made (one per sentence/segment), which is generally faster and more cost-effective.

   • Cons:

     • Alignment Challenge: The primary difficulty is that Google's NMT does not provide a direct word-to-word or phrase-to-phrase mapping in its standard API response. Perfect alignment for an interlinear display is often an extremely complex, if not impossible, computational challenge without the underlying translation model data.

2. Translate Words and Phrases Iteratively (Not Recommended)

   • Process: Break the source text down into individual words or small phrases and make a separate API call for each one. Then, display the original word and its corresponding translation.

   • Pros:

     • Simple Alignment: Alignment is trivial since each call corresponds to a single word/phrase.

   • Cons:

     • Poor Translation Quality: Google's NMT is specifically designed to work on full sentences to understand context.³ Translating isolated words or short phrases loses context, resulting in poor, literal, and often incorrect translations.⁴

     • Inefficiency & Cost: This dramatically increases the number of API calls, leading to higher latency and significantly greater costs.

---

Conclusion

Since Google Translate's core strength is its contextual Neural Machine Translation, you should prioritize translation quality by sending the full segment. The trade-off is that you will need to implement a sophisticated (and likely imperfect) client-side logic to attempt to align the words for the interlinear display.

The other strategy involves losing the key benefit of Google Translate's technology.

To see how to get started with the Google Translate API, watch ... video: How to use the Google API to translate texts. This video is relevant because it demonstrates using the Google Translate API, which is the necessary first step for either strategy.

Environment and AI solutions

 

20.09.25, ref. per Gemini

Question. Which is more friendly to the environment, for example in terms of electricity usage, carbon footprint, etc., client-side or server-side AI solutions? 

Response. In general, client-side AI solutions are more environmentally friendly than server-side solutions, but the answer is complex and depends on many factors. Client-side solutions, which run on a user's local device, eliminate or reduce the need for massive, energy-intensive data centers for inference (the use of a trained AI model). However, the training of these models, regardless of where they are ultimately deployed, remains a major environmental concern.

***

### Server-Side AI: The Environmental Burden

Server-side AI relies on large-scale data centers, which are one of the biggest contributors to the environmental footprint of AI. These data centers consume immense amounts of electricity for three main purposes:

* **Training and Inference:** The process of training a large language model (LLM) like GPT-4 can consume a staggering amount of energy. While a single query (inference) uses a tiny fraction of that energy, the sheer volume of billions of daily queries for popular models adds up to a massive cumulative carbon footprint.
* **Cooling:** Data centers generate a tremendous amount of heat. To prevent hardware from overheating, they require sophisticated and highly energy-intensive cooling systems, which can also consume millions of gallons of water annually.
* **Idle Capacity:** To ensure high availability and responsiveness, data centers must maintain a degree of idle capacity, which still consumes energy while waiting to handle traffic spikes.

The carbon footprint of server-side AI also depends on the **location** of the data center and its energy source. A data center running on a grid powered by renewable energy like wind or solar will have a significantly lower carbon footprint than one relying on fossil fuels.

### Client-Side AI: The Greener Alternative

Client-side, or on-device, AI runs directly on a user's device, such as a smartphone or laptop. Because the processing happens locally, it avoids the energy consumption and carbon emissions associated with data transmission and large data centers.

While client-side AI is more efficient for **inference**, it's crucial to understand a few key points:

* **Training is still a major factor:** The initial training of the AI model, whether it's for on-device or server-side use, is the most energy-intensive part of the process. This training is almost always done in a data center.
* **Limited Capabilities:** On-device models are typically smaller and less powerful than their server-side counterparts. This is because consumer devices lack the massive computational power and memory of a data center.
* **Device-level Consumption:** While they bypass the data center, on-device models still use a device's battery and CPU, which consumes energy. This can lead to faster battery drain and generate some heat.

### A Nuanced Comparison

Ultimately, the choice between client-side and server-side AI for a given application is a trade-off between performance and environmental impact. For computationally demanding tasks like training a large generative AI model, a server-side solution is currently the only option. For tasks that can be performed by smaller models, such as basic text completion or image classification, client-side AI is a much more energy-efficient and environmentally responsible choice.

As on-device hardware becomes more powerful and AI models are optimized to be smaller and more efficient, we'll likely see a shift toward more client-side solutions, reducing the reliance on energy-hungry data centers for many everyday AI applications.

Musing futures

Long-form interlinear translations are available online, that is ones not having to do with biblical or scriptural texts. Fiction mostly, but there is some non-fiction to be found. The interlinear examples, many published for online or physical book reading, show words/phrases aligned with the source's text. This is a first step toward getting closer and closer to contextual meaning, e.g., based on historical understanding of language items, hermeneutics, etymology of a word, ngram (frequency of word usage over time), etc.

Which of the current tools included in the set of experiments shows promise for use in creating long-form interlinear translations (of course, here we refer to the softened understanding of interlinear here termed inter-linea). For discussion purposes, see https://jkm.free.nf/inter-linea/w4word.html.

A product of the above word/phrase approach based on character count limitation per line is below, without entirely correct alignment and no glossing, but serviceable I believe. If so, perhaps a more rigorous(?) translation service might be employed, something like DeepL? Then the word/phrase approach (versus others on the same site) might function as a prototype for long-form translations . . . to publish?

In essence, Bohr and Heisenberg argued that science had finally caught up with the conceptual problems involved in the description of reality that philosophers had been warning of for centuries. Bohr is quoted as saying: "There is no quantum world. There is only an abstract quantum physical description. It is wrong to think that the task of physics is to find out how nature is. Physics concerns what we can say about nature." This vaguely positivist statement was echoed by Heisenberg:. "[W]e have to remember that what we observe is not nature in itself but nature exposed to our method of questioning." 

Their broadly anti-realist "Copenhagen interpretation" "denying" that the wave function represents the real physical state of a quantum system quickly became the dominant way of thinking about quantum mechanics. More recent variations of such anti-realist interpretations suggest that the wave function is simply a way of "coding" our experience, or our subjective beliefs derived from our experience of the physics, allowing us to use what we've learned in the past to predict the future.

The word/phrase display.

In essence, Bohr and Heisenberg argued that
Im Wesentlichen argumentierten Bohr und Heisenberg das

science had finally caught up with the conceptual
Die Wissenschaft hatte endlich das Konzept eingeholt

problems involved in the description of reality
Probleme, die an der Beschreibung der Realität verbunden sind

that philosophers had been warning of for
dass Philosophen vor gewarnt hatten

centuries. Bohr is quoted as saying: "There is no
Jahrhunderte. Bohr wird mit den Worten zitiert: "Es gibt keine

quantum world. There is only an abstract quantum
Quantenwelt. Es gibt nur ein abstraktes Quantum

physical description. It is wrong to think that
Physische Beschreibung. Es ist falsch zu denken, dass

the task of physics is to find out how nature is.
Die Aufgabe der Physik ist es, herauszufinden, wie die Natur ist.

Physics concerns what we can say about nature."
Die Physik betrifft das, was wir über die Natur sagen können. "

This vaguely positivist statement was echoed by
Diese vage positivistische Aussage wurde von wiederholt von

Heisenberg:. "[W]e have to remember that what we
Heisenberg:. "[Wir müssen uns daran erinnern, was wir

observe is not nature in itself but nature
beobachten ist nicht die Natur an sich, sondern die Natur

exposed to our method of questioning."
unserer Befragungsmethode ausgesetzt. "

Their broadly anti-realist "Copenhagen
Ihr weitläufig anti-realistischer "Kopenhagen"

interpretation" "denying" that the wave function
Interpretation "" leugnen ", dass die Wellenfunktion fungiert

represents the real physical state of a quantum
repräsentiert den wirklichen physischen Zustand eines Quanten

system quickly became the dominant way of
Das System wurde schnell zur dominierenden Art von

thinking about quantum mechanics. More recent
Denken Sie an die Quantenmechanik. Neuer

variations of such anti-realist interpretations
Variationen solcher antirrealistischer Interpretationen

suggest that the wave function is simply a way of
Schlagen Sie vor, dass die Wellenfunktion einfach eine Möglichkeit ist

"coding" our experience, or our subjective
"codieren" unsere Erfahrung oder unsere subjektive

beliefs derived from our experience of the
Überzeugungen abgeleitet aus unserer Erfahrung der

physics, allowing us to use what we've learned in
Physik, damit wir das verwenden können, was wir gelernt haben

the past to predict the future.
die Vergangenheit, die Zukunft vorherzusagen.

An enhancement to this display might be to show the target before the source, where one wanted to create an inter-linea for more than personal use. And one of the two might be in a smaller font or colored in some way to reduce distraction/dependence on one rather than the other language. 

Some interesting links

 [This post is open-ended. As links become known and are assessed relevant to the discussions here, they will be added. Got something to suggest?] 

https://eurolinguiste.com/interlinear-books-review-a-dual-language-book/

https://interlinearbooks.com/

https://www.alexanderarguelles.com/academy/

https://textanalysisonline.com/nltk-sentence-segmentation 

And here are some more with short notes.

https://readlang.com/
nifty online teaching/learning using interlinear concepts and web page technologies

https://interlinearbooks.com/tools/interlinearizer/
multi-step process on- and off-line, labor intensive

https://paratext.org
multi-step process within a software program using training corpora

https://github.com/gbroques/interlinearizer/blob/main/README.md
may be useful in building interlinear, aligned translations

https://www.lucalampariello.com/create-interlinear-texts/
Imtranslator enthusiast, recommended

https://interlinearbooks.com/tools/interlinearizer/splitter/
text splitter, useful

https://github.com/lukastk/interlinearize
relies on Calibre conversions, looks promising as local/server-side solution for longer works 

Hyrskykari, Aulikki & Majaranta, Päivi & Räihä, Kari-Jouko. (2005). From gaze control to attentive interfaces. Interactive applications that make use of eye tracking have traditionally been based on command-and-control. Applications that make more subtle use of eye gaze have recently become increasingly popular in the domain of attentive interfaces that adapt their behaviour based on the visual attention of the user. We provide a review of the main systems and application domains where this genre of interfaces has been used.

OpenAI API key

[Check the OpenAI website for the latest . . . this is an old post.]

Here's a step-by-step guide to obtain an OpenAI API key and considerations for using it in a web application:

---

### **Steps to Get an OpenAI API Key**

#### **1. Create an OpenAI Account**
1. Go to [OpenAI's website](https://openai.com/).
2. Click **Sign Up** to create an account, or **Log In** if you already have one.

#### **2. Access the API Section**
1. Once logged in, navigate to the OpenAI **API Dashboard** at [https://platform.openai.com/](https://platform.openai.com/).
2. Click on your profile picture in the top-right corner and select **API Keys** from the dropdown menu.

#### **3. Generate an API Key**
1. On the API Keys page, click the **Create new secret key** button.
2. A new API key will be generated. Copy the key immediately, as it will only be displayed once.
   - Save the key securely (e.g., in a password manager).

#### **4. Set Up Billing**
1. Go to the **Billing** section in your account settings.
2. Add a valid payment method to enable API usage.
   - Note: OpenAI offers a free trial for new users with a limited amount of credits. Check the specific details on their pricing page.

---

### **Considerations for Using the API Key**

#### **1. **Costs**
- **Pay-as-you-go model**: You will be charged based on usage.
- Refer to OpenAI’s [pricing page](https://openai.com/pricing) to understand the costs per token for different models (e.g., GPT-4, GPT-3.5).
- Monitor usage carefully to avoid unexpected charges by setting usage limits in the **Usage** section of your dashboard.

#### **2. **Rate Limits**
- OpenAI imposes rate limits on API usage. Check your account for specific limits and adjust your use accordingly.

#### **3. **Compliance**
- Review OpenAI's **Usage Policies** to ensure you comply with their guidelines, particularly when dealing with translations that may involve sensitive or copyrighted content.

---

### **Implementation in the Web App**

Users input their API key to perform translation requests. In doing so:

1. **Input Field for API Key**
   - You have a secure input field where you can enter your API key.
   - User's API keys are not saved on the server.

2. **Translation Request**
   -Translations and displays are not guaranteed one hundred percent accurate. 
   -Proofread results. Report any problems . . . so we (OpenAI and the web app provider) can improve.

Client-side web apps--try 'em

 FOR TESTING

Available as they develop.

https://jkm.free.nf/inter-linea

May be deactivated without notice.

Feedback appreciated.

final.copy@yahoo.com

DIY sentence-by-sentence translation

You have your source text and you have the target translation. However, they are separate, as one would see them side-by-side from, let's say, Google Translate. How do you get a sentence-by-sentence display for closer reading and understanding of your source and its translation?

You can use this tool right here, or from whence it comes, jkm.free.nf/inter-linea/sidebyside.html.

A kind of DIY quick fix kindly made available free.

 

Interlinear Text Display

Source Text

Target Translation

Display Order: Source → Target Target → Source

Enter your source text and translation above, then click "Generate Interlinear" to see the sentence-by-sentence display.

Concept view, line-by-line alt-trans

This is a generic template to visualize or build an actual web (html) app. You can try it, but it won't produce a translation nor a display. A working version may be coming soon, here or elsewhere.

 

Interlinear Translator (Client-Side Demo)

Source Language (ISO code): Target Language (ISO code): Input Text:

Word/char count?

For reference--English

Based on the average word length of 5 to 6 characters (including spaces and punctuation), 300,000 characters is approximately 50,000 to 60,000 words.

Proofreading, copyediting, etc.*

[In reference to a another post here having to do with  reduction in demand for people-type translators . . . these people/roles below are still needed. AI and automation can't perform this work, yet. I don't think developing technologies will any time soon if ever.]

Copywriting and proofreading are two distinct activities in the realm of writing and editing. Here's an overview of their differences.

Copywriting

Copywriting refers to the process of crafting written content, typically for advertising or promotional purposes. The primary goal of copywriting is to engage the audience, persuade them, and drive them to take a specific action. Copywriters create content for various mediums such as advertisements, websites, brochures, social media campaigns, and more. They focus on conveying a compelling message, highlighting the benefits of a product or service, and employing persuasive techniques to capture attention and elicit desired responses. Copywriters often work closely with marketing teams to align their writing with the overall brand strategy and target audience.

Proofreading

Proofreading, on the other hand, involves reviewing and correcting written material to identify errors and ensure accuracy, consistency, and clarity. Proofreaders examine text for grammatical mistakes, spelling errors, punctuation issues, and typographical errors. They also check for proper formatting, adherence to style guidelines, and overall readability. Proofreading is typically done after the writing phase to polish the content and make it error-free. Proofreaders have a keen eye for detail and possess strong language skills, enabling them to spot errors that might have been overlooked during the writing process. 

In summary, copywriting focuses on creating persuasive and engaging content for marketing and promotional purposes, while proofreading concentrates on reviewing and correcting written material to eliminate errors and enhance readability. Both copywriting and proofreading play essential roles in producing high-quality written content.

---

What is the difference between a copyeditor and editor?

The terms "copyeditor" and "editor" are often used interchangeably, but there are subtle differences between the two roles. Here's an explanation of their distinctions.

Copyeditor

A copyeditor is responsible for reviewing written material, such as articles, manuscripts, or documents, with a focus on improving the overall quality, accuracy, and consistency of the text. Copyeditors meticulously examine the content line by line and make corrections or revisions to ensure clarity, coherence, and adherence to grammar and style guidelines. They pay attention to grammar, spelling, punctuation, syntax, and usage errors. Additionally, copyeditors may suggest improvements in sentence structure, word choice, and overall readability. They work closely with the author to maintain the author's voice and intent while enhancing the presentation of the material.

Editor

An editor, in a broader sense, can encompass various roles and responsibilities depending on the context. Editors can be involved in different stages of the writing process, including content development, structural editing, copyediting, and proofreading. They often oversee the entire editing process and collaborate with writers to shape the content In essence, a copyeditor primarily focuses on improving the technical aspects of the written material, such as grammar and style, while an editor encompasses a broader role, often involved in guiding and shaping the content's structure, organization, and overall impact. 

However, it's important to note that the specific roles and responsibilities can vary across different contexts and publications.and ensure its effectiveness. Editors may provide feedback on the overall structure, organization, and flow of the material. They may also address issues related to tone, audience engagement, and narrative consistency. Editors have a comprehensive understanding of the target audience and the publishing industry and may contribute to decision-making regarding content selection and overall direction.

In essence, a copyeditor primarily focuses on improving the technical aspects of the written material, such as grammar and style, while an editor encompasses a broader role, often involved in guiding and shaping the content's structure, organization, and overall impact. However, it's important to note that the specific roles and responsibilities can vary across different contexts and publications.

Hiatus--non-essential reading

 

This blog, yes, disappeared for a bit. The reason was that feedback indicated two "consumer" interests, interlinear short passage translations and the same format for longer works, as in novels and non-fiction/academic articles,. In addition, chatbots, as I have called them, and all manner of other AI translation options flooded the scene--the choices and developments became too much to manage. 

My inability to keep up with the above as well as developing and testing my own modest server-side efforts based on what I then knew, and found wanting, well, you see why hiatus.

As of this date, with the help of advances in AI coding capabilities on offer and my own take on how sophisticated a product needed to be to meet the needs of what I have identified as the rest of us, I am back and have progress to report on several fronts (about that not in this post).

During the hiatus and now still, translation view (make 'em) tools and corpora of works/literature, other than scriptural--"interlinears" for the rest of us--are few and far between. Some noteworthy possibilities there are. For example,

• interlinearhub dot com
•  
•  

 What might be a reason for this? Something to do with reader purposes and comfort and efficiency. (See https://interlineardotworld.blogspot.com/2025/08/language-learning-and-translation.html)

The development a tools for creating/having one or another kind of interlinear translation view has evolved into two tech alternatives and a new set of options under the broad title of interlinear.

Accessible to all via web applications will be either client- or server-side solutions. (Earlier server-side solution proponents over-hyped and -promised their ways, which were many and in the final run proprietary! Silly and yikes!) 

As of this writing, I have a basic client-side demonstration web app to share (see separate post). And I will develop a server-side solution that will take advantage of improved AI-assisted coding and the build-once, run-anywhere criterion (wish me luck).

Not finally, a proposed change in the rest-of-us categories for what we might find a best fit and more loosely construed notion of interlinear.

•  line by line
•  sentence by sentence
•   word/phrase by word/phrase
•   aligned
•   inline
•   per paragraph, over under
•   from side by side to a form of interlinear

The original idea is represented by this early draft of the project, so-called, in '24.

Finally, a recent podcast by Grammar Girl having to do with AI and translation, she interviewed a Dutch translator. The short of it is , that according to the guest, ChatGPT is uncannily good at translating these days, and the job market for translators in the Netherlands and elsewhere is drying up, a trend that has increasingly been felt in Europe in the last three to five years. There is still room for proofreading translations, but this niche work has never  earned the few practitioners sufficient money to live on. The guest interviewed by Grammar Girl indicated that there would always be a market for literary translators, for such textural art takes skills that AI still has not achieved, if we can say that AI possesses skills. (If curious, See what ChatGPT had to contribute to one of my literary efforts.)

Hiatus end. Onward.

 

Language learning and translation displays

QUESTION. Is there research about language learning using translations displayed
or printed in different ways, for example side by side versus in interlinear fashion?*

RESPONSE: [T]here has been research into the effectiveness of different translation
display formats, such as side-by-side (parallel text) and interlinear text, for language
learning. These studies generally focus on how the arrangement of translations can
impact comprehension, retention, and the learner’s ability to acquire new vocabulary
and grammar structures. Here’s a brief overview of findings related to these formats:
### 1. **Parallel (Side-by-Side) Texts**
- **Advantages**:
- Allows the learner to compare the original language and translation easily.
- Enhances understanding of structure, as learners can see how ideas are expressed
differently across languages.
- Encourages active engagement as the learner switches between languages to
decipher meaning.
- **Disadvantages**:
- Can cause learners to rely heavily on the translation, reducing the need to
struggle with the original language.
- Switching focus between two columns can slow down reading fluency and
comprehension in the target language.
**Research Findings**:
- Learners often benefit from side-by-side texts when they are at a beginner or
intermediate level, as it facilitates understanding and reduces cognitive load.
- A 2017 study showed that parallel texts help learners notice differences in
sentence structures and word usage, improving grammatical understanding.
### 2. **Interlinear Texts (Word-for-Word Translation Below the Original)**
- **Advantages**:
- Gives a word-by-word or phrase-by-phrase translation, which can help learners
focus more on sentence construction and vocabulary acquisition.
- Learners do not need to switch between two columns, allowing for smoother
reading of the original text.
- **Disadvantages**:
- May interfere with comprehension since the translations are more literal, which
can make understanding the natural flow of the target language difficult.
- Learners may become dependent on the direct translations rather than processing
the meaning holistically.
**Research Findings**:- Interlinear texts are more effective for acquiring vocabulary, especially for
learners at an early stage, because they expose them to the structure and syntax of the
target language.
- A 2019 study indicated that learners using interlinear texts often focus more on
individual word meanings and syntax, which can slow down comprehension but
deepen understanding of linguistic patterns.
### 3. **Cognitive Load and Efficiency**
- Research into *cognitive load theory* in language learning has found that how
translations are displayed can affect how much mental effort is required to learn new
material.
- Studies suggest that interlinear texts can reduce cognitive load for beginner
learners, but parallel texts are often more useful as learners advance and need to focus
on broader context and meaning.
### 4. **Mixed Methods (e.g., Bilingual Reading with Occasional Interlinear
Assistance)**
- Some studies propose using a hybrid approach, where learners use parallel texts
for general comprehension and interlinear texts for detailed vocabulary or grammar
studies.
- A 2021 research paper indicated that switching between these formats can be
especially effective for learners who struggle with comprehension but are ready to
move beyond beginner vocabulary.
In summary, both side-by-side and interlinear formats have their strengths depending
on the learner’s level and the goals of the language learning task. Interlinear texts are
often better for detailed linguistic study and vocabulary building, while side-by-side
texts are helpful for general comprehension and sentence structure comparison.
__________
* https://chatgpt.com/share/67160e03-5980-8009-b225-48bdba63de25
generated 21.10.2024 (link may not be available as of this post)

It matters

The prayer of St. Francis of Assisi, "his" Peace Prayer, among its titles over the years, became my go-to for meditation in the 1980s, and often still is. However, when I learned passage meditation and began practice, the version of the prayer was not as I knew it growing up in a rather strict Catholic family and my fourteen years of parochial* schooling. With the new version I adopted and use, the last line has given me pause, and acts as distraction, in my modest attempts at ascendancy.

The line I knew was: "and it is in dying that we are born to eternal life." The line as given by my spiritual teacher was: "It is in dying to self [or Self] that we are born to eternal life."

The difference makes a difference, for when I think back, two members of my family held dear the version we knew, and no doubt they prayed with those words. One of those people wished for death to come soon, and I thought she expressed that wish by embracing that prayer. This is conjecture or opinion, I admit. Perhaps this difference is only significant in my own mind.

The difference is different, however, because both versions are incomplete translations of the French original. (To complete the deception, there is no evidence it was a prayer we can attribute to the historical St. Francis.)

Am I too old or stuck to learn by heart the original in French or its English translation? I can give it a try, as I reflect on the importance of getting words/meanings as close to correct as possible from the start. It is important to translate accurately with meaning and intent the full original source to the target language of choice and text's adoption for use. Agree?

In short, it matters. Interlinear translations quickly reveal whether or not meaning and intent have been faithfully rendered if one knows the target language and is familiar with or is more than comfortable reading in the source language. And even if this is not the case, it matters whether for Christians the Virgin Mary was a virgin and in what sense.