Morphemes (prefixes, suffixes, root words) are linguistic descriptions, defined as the smallest meaningful unit of words. Our proposed shared task is morpheme segmentation that converts a text into a sequence of morphemes. In order to prepare a dataset for this task, we integrated all basic types of morphological databases (including UniMorph (Kirov et al., 2018b; McCarthy et al., 2020) – inflectional morphology; MorphyNet (Batsuren et al., 2021) – derivational morphology; Universal Dependencies (Nivre et al., 2017) and ten editions of Wiktionary – compound morphology and root words). In the future, we expect the NLP community will benefit a lot by innovating subword-based tokenization with this task. This shared task has two parts:

• Part 1: Word-level morpheme segmentation
• Part 2: Sentence-level morpheme segmentation

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Two subtasks will be scored separately. Participant teams may submit as many systems as they want to as many subtasks as they want.

At the word level, participants will be asked to segment a given word into a sequence of morphemes. Input words contains all types of word forms: root words, derived words, inflected words, and compound words.

Training and development data are UTF-8-encoded tab-separated values files. Each example occupies a single line and consists of input word, the corresponding morpheme sequence, and the corresponding morphological category. The following shows three lines of English data:

inaccuracies  in @@accurate @@cy @@s  110
dictionary  dictionary  000
screwdriver screw @@drive @@er  011

Note: The third column as the morphological category is an optional feature that can only be used to oversample or undersample training data.

First example is a derived word with prefix (in-) and suffixes (-cy and -s), and second example is a root word. Third example is a compound word. In the test datasets, we will provide only first column of data as input words.

Development languages are:

1. ces: Czech
2. eng: English
3. fra: French
4. hun: Hungarian
5. spa: Spanish
6. ita: Italian
7. lat: Latin
8. rus: Russian

For some of the development languages, we are providing the word categories so that participant can deal with imbalanced situation of morphological categories.

The following table shows the word-level task results of pretrained BertTokenizer on English. This pretrained model was taken from HuggingFace.

At the sentence level, participating systems are expected to predict a sequence of morphemes for a given sentence. The following shows two lines of English data:

Six weeks of basic training. Six week @@s of base @@ic train @@ing .
Fistfights, please. Fist @@fight @@s , please .

The following shows two lines of Mongolian data:

Гэрт эмээ хоол хийв. Гэр @@т эмээ хоол хийх @@в .
Би өдөр эмээ уусан. Би өдөр эм @@ээ уух @@сан .

In above example, эмээ is a hononym of two different words, first means a grandmother and second is medicine. Depending on the context, the second homonym word is inflectional form of medicine and it is segmentable.

Development languages are:

1. ces: Czech
2. eng: English
3. mon: Mongolian

We will provide python evaluation scripts, reporting the following evaluation measures:

• Precision - fraction of correctly predicted morphemes on all predicted morphemes
• Recall - ratio of correctly predicted morphemes on all gold morphemes
• F-measure - the harmonic mean of the precision and recall
• Edit distance - average Levenshtein distance between the predicted output and the gold instance.

• February 23, 2022: Training splits for development languages are released at https://github.com/sigmorphon/2022SegmentationST/tree/v0.1
• February 28, 2022: Development splits for development languages are released at https://github.com/sigmorphon/2022SegmentationST/tree/v0.1
• March 5 March 12, 2022: Baseline code, and results released.

• April 8, 2022: Training and development splits for surprise languages released.

• April 15, 2022: Test splits for development and surprise languages are released.
• April 29, 2022: Participants' submissions due.

• May 13, 2022: Participants' draft system description papers due.
• May 20, 2022: Participants' camera-ready system description papers due.

• Khuyagbaatar Batsuren (National University of Mongolia)
• Gábor Bella (University of Trento)
• Aryaman Arora (Georgetown University)
• Viktor Martinović (University of Vienna)
• Kyle Gorman (Graduate center, City University Of New York)
• Zdeněk Žabokrtský (Charles University)
• Amarsanaa Ganbold (National University of Mongolia)
• Šárka Dohnalová (Charles University)
• Magda Ševčíková (Charles University)
• Kateřina Pelegrinová (University of Ostrava)
• Fausto Giunchiglia (University of Trento)
• Ryan Cotterell (ETH Zürich)
• Ekaterina Vylomova (University of Melbourne)

The data is released under the Creative Commons Attribution-ShareAlike 3.0 Unported License inherited from Wiktionary itself.

Kirov, C., Cotterell, R., Sylak-Glassman, J., Walther, G., Vylomova, E., Xia, P., Faruqui, M., Mielke, S., McCarthy, A., Kübler, S., Yarowsky, D., Eisner, J., and Hulden, M. (2018). UniMorph 2.0: Universal Morphology. Proceedings of LREC 2018.

McCarthy, A.D., Kirov, C., Grella, M., Nidhi, A., Xia, P., Gorman, K., Vylomova, E., Mielke, S.J., Nicolai, G., Silfverberg, M. and Arkhangelskij, T., (2020). UniMorph 3.0: Universal Morphology.. Proceedings of LREC 2020.

Batsuren, K., Bella, G. and Giunchiglia, F., (2021). MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology. In Proceedings of SIGMORPHON 2021 (pp. 39-48).

Nivre, J., Agić, Ž., Ahrenberg, L., Antonsen, L., Aranzabe, M.J., Asahara, M., Ateyah, L., Attia, M., Atutxa, A., Augustinus, L. and Badmaeva, E., (2017). Universal Dependencies 2.1.