aipl VS apertium

AIPL is an "Array-Inspired Pipeline Language", a tiny DSL in Python to make it easier to explore and experiment with AI pipelines.

https://github.com/saulpw/aipl

When you want to run some prompts through an LLM over a dataset, with some preprocessing and/or chaining prompts together, AIPL makes it much easier than writing a Python script.

Yes! This is why I started working on AIPL. The scripts are much more like recipes (linear, contained in a single-file, self-evident even to people who don't know the language). For instance, here's a multi-level summarizer of a webpage: https://github.com/saulpw/aipl/blob/develop/examples/summari...

The goal is to capture all that knowledge that langchain has, into consistent legos that you can combine and parameterize with the prompts, without all the complexity and boilerplate of langchain, nor having to learn all the Python libraries and their APIs. Perfect for prototypes and experiments (like a notebook, as you suggest), and then if you find something that really works, you can hand-off a single text file to an engineer and they can make it work in a production environment.

I agree, and that's why I've been working on AIPL[0]. Our first v0.1 release should be in the next few days. https://github.com/saulpw/aipl

It's basically just a simple scripting language with array semantics and inline prompt construction, and you can drop into Python any time you like.

I also was underwhelmed by langchain, and started implementing my own "AIPL" (Array-Inspired Pipeline Language) which turns these "chains" into straightforward, linear scripts. It's very early days but already it feels like the right direction for experimenting with this stuff. (I'm looking for collaborators if anyone is interested!)

https://github.com/saulpw/aipl

This is very cool, looking forward to it! I've been doing the same thing with Spanish Wikipedia articles for a while, using a few lines of Bash + Regex. I was using Apertium for it. https://apertium.org/ It's definitely worse than most ML-based solutions, but it works reliably and fast; you can run it entirely offline. With Spanish translations, the main problem I was facing is lack of vocabulary, so I created https://github.com/phil294/apertium-eng-spa-wiktionary which about doubles the amount of recognized words, albeit with wonky grammar.

I used to keep track of the state of machine translation some years back.

I think the way you measure the success of an automated translation is edit distance, i.e. how many manual edits you need to make to a translated text before you reach some acceptable state. I suppose it's somewhat subjective, but it is possible to construct a benchmark and allow for multiple correct results.

The best resources I knew back then were:

VISL's CG-3 self-reported a competitively low edit distance compared to Google Translate: https://visl.sdu.dk/constraint_grammar.html -- the abstraction unfortunately requires a rather deep knowledge of any one particular language's grammar. It is a convincing argument that in order to beat Google Translate, you want less fuzzy machine learning and more structural analysis. But you also need a PhD in computational linguistics and deep knowledge of each language.

Apertium has an open-source pipeline: https://apertium.org/ -- seems to be much more like an open-source approach with a quality similar to Google Translate (although I don't know if it's better or worse; probably slightly worse in most cases, and with a slightly lower coverage).

For context, I have been contributing CV Creole data to Unicode's CLDR and MediaWiki for a number of years now, but both are mostly manual work. I once considered setting up an Apertium language pair between CV Creole and Portuguese, given the grammatical similarities, but never got around to it.

Lingva is awesome. Also don't forget to check out LibreTranslate and Apertium. They are open source. Apertium can even translate web pages (you need to enter the URL).

#!/bin/bash set -x mkdir -p apertium-src && \ mkdir -p $MTDIR cd apertium-src && \ wget http://ftp.tsukuba.wide.ad.jp/software/gcc/releases/gcc-8.5.0/gcc-8.5.0.tar.gz -O - \ | gzip -dc \ | tar -xf - && \ cd gcc-8.5.0 && \ ./configure --prefix=$MTDIR --disable-multilib && \ make -j $(nproc) && \ make install && \ cd .. || exit 1 cd apertium-src && \ wget https://github.com/unicode-org/icu/releases/download/release-69-1/icu4c-69_1-src.tgz -O - \ | gzip -dc \ | tar -xf - \ && cd icu/source \ && CC=gcc CXX=g++ ./configure --prefix=$MTDIR \ && CC=gcc CXX=g++ make -j $(nproc) \ && CC=gcc CXX=g++ make install \ && cd ../.. \ || exit 1 cd apertium-src && \ svn checkout http://beta.visl.sdu.dk/svn/visl/tools/vislcg3/trunk vislcg3 && \ cd vislcg3 && ./get-boost.sh \ && ./cmake.sh -DCMAKE_INSTALL_PREFIX=$MTDIR \ -DICU_INCLUDE_DIR=$MTDIR/include \ -DICU_LIBRARY=$MTDIR/lib/libicuuc.so \ -DICU_IO_LIBRARY=$MTDIR/lib/libicuio.so \ -DICU_I18N_LIBRARY=$MTDIR/lib/libicui18n.so \ && make -j$(nproc) && \ make install && cd .. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/lttoolbox && \ cd lttoolbox && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium && \ cd apertium && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-lex-tools && \ cd apertium-lex-tools && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha && \ cd apertium-tha && ./autogen.sh --prefix=$MTDIR && make && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha-eng && \ cd apertium-tha-eng && ./autogen.sh --prefix=$MTDIR && make && make install && cd .. && \ cd .. || exit 1