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The SIGMORPHON 2016 Shared Task—Morphological Reinflection
TLDR
The 2016 SIGMORPHON Shared Task was devoted to the problem of morphological reinflection and introduced morphological datasets for 10 languages with diverse typological characteristics, showing a strong state of the art.
Joint Lemmatization and Morphological Tagging with Lemming
TLDR
LEMMING sets the new state of the art in token-based statistical lemmatization on six languages and reduces the error by 60%, and gives empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial.
Gender Bias in Contextualized Word Embeddings
TLDR
It is shown that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus and two methods to mitigate such gender bias are explored.
Information-Theoretic Probing for Linguistic Structure
TLDR
An information-theoretic operationalization of probing as estimating mutual information that contradicts received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation.
Labeled Morphological Segmentation with Semi-Markov Models
TLDR
A new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics are introduced.
Weighting Finite-State Transductions With Neural Context
TLDR
This work proposes to keep the traditional architecture, which uses a finite-state transducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks, and defines a probability distribution over aligned output strings in the form of a weighted finite- state automaton.
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
TLDR
This work presents a novel approach for converting between masculine-inflected and feminine-inflection sentences in morphologically rich languages and shows that it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.
UniMorph 3.0: Universal Morphology
TLDR
Advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018 are detailed.
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
TLDR
The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages and showed that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data.
Cross-lingual Character-Level Neural Morphological Tagging
TLDR
This work explores a transfer learning scheme, whereby character-level recurrent neural taggers are trained to predict morphological taggings for high-resource languages and low- Resource languages together, which enables knowledge transfer from the high- resource languages to the low-resource ones.
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