Dear all,
The 5th International Workshop on Computational Approaches to Historical
Language Change (https://www.changeiskey.org/event/2024-acl-lchange/,
collocated with ACL'24) is hosting a shared task on _explainable
semantic change modeling_: AXOLOTL-24.
AXOLOTL-24 stands for "Ascertain and eXplain Overhauls of the Lexicon
Over Time at LChange'24" and you are welcome to participate!
https://github.com/ltgoslo/axolotl24_shared_task will serve as the main
information hub for the shared task. Example of the datasets, processing
and evaluation scripts, etc will appear in this Github repository in due
time according to the timeline below.
If you are interested in AXOLOTL-24, please also join our Google Group:
https://groups.google.com/g/axolotl-24/
========
Timeline
========
- February 1 2024 - training data published
- March 25 2024 - test data published
- April 9 2024 - deadline for submission of the systems’ predictions
- April 10 2024 - AXOLOTL'24 test results published
- May 10 2024 - paper submission deadline (same procedure as with other
LChange'24 papers)
============
Introduction
============
This shared task builds on the existing tradition of competitions in
diachronic semantic change detection, like (Schlechtweg et al 2020) and
many others. However, this time we focus on explaining diachronic
semantic changes, even if on a very basic level (for now).
In particular, we challenge the participants to implement a semantic
change modeling system which, given two historical corpora and a sense
inventory corresponding to one of the periods, is able to:
1. Find the target word usages associated with new, gained senses
2. Describe these senses in a way that facilitates understanding and
lexicographical research.
Thus, the task is to identify which exact senses were gained between two
time periods and generate reasonable descriptions (definitions) of these
senses.
To be able to use high-quality gold data, we use a simplified setup
where instead of asking the participants to retrieve and analyze all
target word usages in raw corpora, we provide two manually checked sets
of usage examples (still of considerable size). Below, we still call
them "corpora", for clarity.
The shared task will feature data from Finnish and Russian languages,
but you do not have to speak these languages to participate. There will
also be a surprise language of lesser size at the test stage. For all
these languages, we will use gold, manually annotated data to evaluate
the predictions of the participant systems.
The shared task will consist of two subtasks. The participants are
welcome to choose one of them or both, at their will.
===============================
Subtask 1. Bridging diachronic word uses and a synchronic dictionary
===============================
The participants are offered two corpora, belonging to different time
periods. In addition to this, they are provided with a set of dictionary
entries (sense inventories) for the target words describing their senses
in the first time period (accompanied by definitions). The task is to
find all usages of the target words belonging to newly gained senses,
i.e., senses not covered by the provided sense inventory.
The assumption is that sense definitions from the dictionary, even
though not always covering all word senses even from the same time
period, may still be a useful additional source of information. The goal
is to map word usages to the dictionary senses. This is very similar to
Word Sense Disambiguation, with the difference being that the usages
corresponding to word senses absent from the dictionary should be
grouped into novel sense clusters (this is more similar to Word Sense
Induction). In a way, this subtask is a mixture of WSD and WSI.
- Inputs: a set of target words, two sets of usages for each target word
(a usage is a text fragment containing a target word); target word
dictionary entries with sense ids for the first of two time periods.
- Predictions: sense id for every word usage of the second time period
(either re-using an id from the provided dictionary or adding a novel one).
- Metrics: Adjusted Rand Index (ARI) for all usages and macro-F1 for
usages with existing senses
- Ground truth: manually annotated sense inventories
==============================
Subtask 2. Definition generation for novel word senses
==============================
This subtask challenges the participants to submit good
descriptions/definitions for the novel senses they found in subtask 1.
The definitions can be generated from scratch or retrieved from existing
ontologies: this is completely up to the participants. The organizers
will map the predicted definitions to the gold standard ones and
evaluate their quality with the standard NLG metrics.
- Inputs: Same as subtask 1
- Predictions: Same as subtask 1 plus a dictionary-like definition for
every novel sense of the target word (a sense not present in the
dictionary entry from the first time period)
- Metrics: BLEU/ROUGE and BERTScore. The final score is averaged across
target words
- Ground truth: definitions from our gold standard sense inventories
==========
Organizers
==========
- Mariia Fedorova (University of Oslo)
- Andrey Kutuzov (University of Oslo)
- Timothee Mickus (University of Helsinki)
- Niko Partanen (University of Helsinki)
- Janine Siewert (University of Helsinki)
==========
References
==========
1. Diachronic word embeddings and semantic shifts: a survey (Kutuzov et
al., COLING 2018)
2. SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
(Schlechtweg et al., SemEval 2020)
3. Computational approaches to semantic change (Tahmasebi et al.,
LangSci Press 2021)
4. Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word
Embeddings (Mickus et al., SemEval 2022)
5. Interpretable Word Sense Representations via Definition Generation:
The Case of Semantic Change Analysis (Giulianelli et al., ACL 2023)
--
Andrey
Language Technology Group (LTG)
University of Oslo
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