2026 modifications to lab 3

lab3/README.md

@@ -12,28 +12,27 @@ Go to [universaldependencies.org](https://universaldependencies.org/) and downlo
 Choose a short (5-10 tokens) and a long (>25 words) sentence and convert it from CoNNL-U to a graphical trees by hand.
 
 ### Step 2: choose a corpus
-Choose one of the two corpora provided in this folder:
+Choose a corpus of 25+ sentences.
 
-- [`comp-syntax-corpus-english.txt`](comp-syntax-corpus-english.txt) is a combination of __English__ sentences from different sources, including [the Parallel UD treebank (PUD)](https://github.com/UniversalDependencies/UD_English-PUD/tree/master). If you want to cheat - or just check your answers - you can look for them in the official treebank. You can also compare your analyses with those of an automatic parser, such as [UDPipe](https://lindat.mff.cuni.cz/services/udpipe/), which you can try directly in your browser. These automatic analyses must of course be taken with a grain of salt
-- [`comp-syntax-corpus-swedish.txt`](comp-syntax-corpus-swedish.txt) consists of teacher-corrected sentences from the [__Swedish__ Learner Language (SweLL) corpus](https://spraakbanken.gu.se/en/resources/swell-gold), which is currently being annotated in UD for the first time. 
-In this case, there is no "gold standard" to check your answers against, but you can still compare your solutions with [UDPipe](https://lindat.mff.cuni.cz/services/udpipe/)'s automatic analyses.
+If you want to start with __English__, you can use[`comp-syntax-corpus-english.txt`](comp-syntax-corpus-english.txt) is a combination of sentences from different sources, including [the Parallel UD treebank (PUD)](https://github.com/UniversalDependencies/UD_English-PUD/tree/master). If you want to cheat - or just check your answers - you can look for them in the official treebank. You can also compare your analyses with those of an automatic parser, such as [UDPipe](https://lindat.mff.cuni.cz/services/udpipe/), which you can try directly in your browser. These automatic analyses must of course be taken with a grain of salt. Note that the first few sentences of this corpus are pre-tokenized and POS-tagged. Each token is in the form `word:<UPOS>`.
 
-In both corpora, the first few sentences are pre-tokenized and POS-tagged. Each token is in the form
 
-`word:<UPOS>`.
+If you want to work with __Swedish__ and might be interested in contributing to an [official UD treebank](https://github.com/universaldependencies/UD_Swedish-SweLL), ask Arianna for [a sample of the Swedish Learner Language corpus](https://spraakbanken.gu.se/en/resources/swell).
+
+If you have other data in mind that you think would be interesting to annotate in UD (not necessarily in English or Swedish), don't hesitate to bring it up during a lab session!
 
 ### Step 3: annotate
 For each sentence in the corpus, the annotation tasks consists in:
 
 1. analyzing the sentence in UD
-2. translating it to a language of your choice
+2. translating it to a language of your choice (as long as one of the two versions is in English or Swedish)
 3. analyzing your translation
 
 The only required fields are `ID`, `FORM`, `UPOS`, `HEAD` and `DEPREL`.
 
 In the end, you will submit two parallel CoNLL-U files, one containing the analyses of the source sentences and one for the analyses of the translations.
 
-To produce the CoNLL-U files, you may work in your text editor (if you use Visual Studio Code, you can use the [vscode-conllu](https://marketplace.visualstudio.com/items?itemName=lgrobol.vscode-conllu) to get syntax highlighting), use a spreadsheet program and then export to TSV, or use a dedicated graphical annotation tool such as [Arborator](https://arborator.grew.fr/#/).
+To produce the CoNLL-U files, you may work in your text editor (you can usually get syntax highlighting by changing the extension to `.tsv`), use a spreadsheet program and then export to TSV, or use a dedicated graphical annotation tool such as [Arborator](https://arborator.grew.fr/#/) (helpful but unstable!).
 
 If you work in your text editor, it might be easier to first write a simplified CoNLL-U, with just the fields `ID`, `FORM`, `UPOS`, `HEAD` and `DEPREL`, separated by tabs, and then expand it to full CoNLL-U with [this script](https://gist.github.com/harisont/612a87d20f729aa3411041f873367fa2) (or similar).
 
@@ -54,7 +53,7 @@ To fully comply with the CoNLL-U standard, comment lines should consist of key-v
 # comment = your comment here
 ```
 
-but for this assigment lines like
+but for this assignment lines like
 
 ```
 # your comment here
@@ -63,24 +62,14 @@ but for this assigment lines like
 are perfectly acceptable too.
 
 ### Step 4: make sure your files match the CoNLL-U specification
-Once you have full CoNLL, you can use [deptreepy](https://github.com/aarneranta/deptreepy/), [STUnD](https://harisont.github.io/STUnD/) or [the official online CoNNL-U viewer](https://universaldependencies.org/conllu_viewer.html) to visualize it.
-
-With deptreepy, you will need to issue the command
-
-`cat my-file.conllu | python deptreepy.py visualize_conllu > my-file.html`
-
-which creates an HTML file you can open in you web browser.
-
-If you can visualize your trees with any of these tools, that's a very good sign that your file _more or less_ matches the CoNNL-U format!
-
-As a last step, validate your treebank with the official UD validator. 
+Check your treebank with the official UD validator. 
 To do that, clone or download the [UD tools repository](https://github.com/UniversalDependencies/tools), move inside the corresponding folder and run
 
 ```
-python validate.py PATH-TO-YOUR-TREEBANK.conllu --lang=2-LETTER-LANGCODE-FOR-YOUR-LANGUAGE --level=1
+python validate.py PATH-TO-YOUR-TREEBANK.conllu --lang=2-LETTER-LANGCODE-FOR-YOUR-LANGUAGE --level=2
 ```
 
-If you want to check for more subtle errors, you can [go up a few levels](https://harisont.github.io/gfaqs.html#ud-validator).
+Level 2 should be enough for part 2, but you can [go up a few levels](https://harisont.github.io/gfaqs.html#ud-validator) to check for more subtle errors.
 
 Submit the two CoNLL-U files on Canvas.
 
@@ -91,7 +80,7 @@ If you want to install MaChAmp on your own computer, keep in mind that very old
 For more information, see [here](https://github.com/machamp-nlp/machamp/issues/42). 
 
 ### Step 1: setting up MaChAmp
-1. optional, but recommended: create a Python virtual environment with the command
+1. create a Python virtual environment with the command
    ```
    python -m venv ENVNAME
    ```
@@ -124,7 +113,7 @@ python scripts/misc/cleanconl.py PATH-TO-A-DATASET-SPLIT
 This replaces the contents of your input file with a "cleaned up" version of the same treebank.
 
 ### Step 3: training
-Copy `compsyn.json` to `machamp/configs` and replace the traning and development data paths with the paths to the files you selected/created in step 2.
+Copy `compsyn.json` to `machamp/configs` and replace the training and development data paths with the paths to the files you selected/created in step 2.
 
 You can now train your model by running
 
@@ -152,4 +141,4 @@ Then, use the `machamp/scripts/misc/conll18_ud_eval.py` script to evaluate the s
 python scripts/misc/conll18_ud_eval.py PATH-TO-YOUR-PART1-TREEBANK predictions/OUTPUT-FILE-NAME.conllu
 ```
 
-On Canvas, submit the training logs, the predictions and the output of `conll18_ud_eval.py`, along with a short text summarizing your considerations on the performance of the parser, based on the predictions themselves and on the output of the results of the evaluation.
+On Canvas, submit the training logs, the predictions and the output of `conll18_ud_eval.py`, along with a short text summarizing your considerations on the performance of the parser, based on the predictions themselves and on the automatic evaluation.