Top Tweets for #sigmorphon
@GwilliamsL in her #SIGMORPHON invited talk tells us about how low level acoustic information remains available even after the brain has a candidate phoneme for 750 ms.
To everyone attending #NAACL2022: Seattle in summertime is an absolute blast (dm if you need recs)
Also: come to #SIGMORPHON! Or attend virtually (details below)
Are you planning on attending SIGMORPHON virtually? If so, we've added important information regarding how to do so to the workshop website: https://t.co/jfDKmSCVp7
#NAACL2022 #NLProc
The effects are dramatic, both in terms of avg accuracy and the ability to discern between systems.
We propose challenging splits, such as the proposed one, to be adopted by future #sigmorphon share tasks @ryandcotterell
Paper: https://t.co/QsNKecBZC8
Huge kudos to Sali for the nice work!
#Nen #Morphology #SIGMORPHON #NLProc
@trevorcohn Paper 1: Modelling Verbal Morphology in Nen
Saliha Muradoglu, Nicholas Evans and Ekaterina Vylomova #ALTA2020 #NLProc
๐ Data: https://t.co/QfRFcOo9bq
Baselines:
๐ Neural (Hard Monotonic Attn.+ Transformer): https://t.co/3XaQqXGAQG
๐ Non-Neural: https://t.co/cCaCxh0r0O
๐ Baseline Accuracy:
https://t.co/bScFIbTgVy
#SIGMORPHON #ACL2020NLP
Back to work! Lanera buelta! Thanks to #CoNLL2018 and #sigmorphon for a great conference and shared task! Now enjoy #emnlp2018!
Erdmann and Habash: as expected, this hybrid approach gains from both the high-recall from the rule-based DeLex and the high precision of the FastText
embeddings. #sigmorphon #emnlp2018
Erdmann and @nyhabash: are data-driven morphology analysis systems enough to replace rule-based methods or are these complementary approaches?
#sigmorphon #emnlp2018
BMS-HAS system at #conll2018 #sigmorphon shared task (Acs et al.) sez: random seeds have more effect than you expect #emnlp2018
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