18 research outputs found
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Corpus-based measures discriminate inflection and derivation cross-linguistically
Japanese passives are traditionally considered to have two types: direct and indirect passives. However, more recent studies, such as Ishizuka (2012), suggest the two types can be unified un- der the same syntactic movement analysis. Uti- lizing the Balanced Corpus of Contemporary Written Japanese (BCCWJ; Maekawa, 2008; Maekawa et al., 2014), this study aims to in- vestigate how likely different types of passives appear in the naturally occurring texts, espe- cially in relation to markedness-based hierar- chy called Noun Phrase Accessibility Hierar- chy (NPAH; Keenan and Comrie, 1977), and to investigate if true indirect passives occur in contemporary written Japanese
Efficient Transformers with Dynamic Token Pooling
Transformers achieve unrivalled performance in modelling language, but remain
inefficient in terms of memory and time complexity. A possible remedy is to
reduce the sequence length in the intermediate layers by pooling fixed-length
segments of tokens. Nevertheless, natural units of meaning, such as words or
phrases, display varying sizes. To address this mismatch, we equip language
models with a dynamic-pooling mechanism, which predicts segment boundaries in
an autoregressive fashion. We compare several methods to infer boundaries,
including end-to-end learning through stochastic re-parameterisation,
supervised learning (based on segmentations from subword tokenizers or spikes
in conditional entropy), as well as linguistically motivated boundaries. We
perform character-level evaluation on texts from multiple datasets and
morphologically diverse languages. The results demonstrate that dynamic
pooling, which jointly segments and models language, is both faster and more
accurate than vanilla Transformers and fixed-length pooling within the same
computational budget
Detecting and Mitigating Hallucinations in Multilingual Summarisation
Hallucinations pose a significant challenge to the reliability of neural
models for abstractive summarisation. While automatically generated summaries
may be fluent, they often lack faithfulness to the original document. This
issue becomes even more pronounced in low-resource settings, such as
cross-lingual transfer. With the existing faithful metrics focusing on English,
even measuring the extent of this phenomenon in cross-lingual settings is hard.
To address this, we first develop a novel metric, mFACT, evaluating the
faithfulness of non-English summaries, leveraging translation-based transfer
from multiple English faithfulness metrics. We then propose a simple but
effective method to reduce hallucinations with a cross-lingual transfer, which
weighs the loss of each training example by its faithfulness score. Through
extensive experiments in multiple languages, we demonstrate that mFACT is the
metric that is most suited to detect hallucinations. Moreover, we find that our
proposed loss weighting method drastically increases both performance and
faithfulness according to both automatic and human evaluation when compared to
strong baselines for cross-lingual transfer such as MAD-X. Our code and dataset
are available at https://github.com/yfqiu-nlp/mfact-summ
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
Most combinations of NLP tasks and language varieties lack in-domain examples
for supervised training because of the paucity of annotated data. How can
neural models make sample-efficient generalizations from task-language
combinations with available data to low-resource ones? In this work, we propose
a Bayesian generative model for the space of neural parameters. We assume that
this space can be factorized into latent variables for each language and each
task. We infer the posteriors over such latent variables based on data from
seen task-language combinations through variational inference. This enables
zero-shot classification on unseen combinations at prediction time. For
instance, given training data for named entity recognition (NER) in Vietnamese
and for part-of-speech (POS) tagging in Wolof, our model can perform accurate
predictions for NER in Wolof. In particular, we experiment with a typologically
diverse sample of 33 languages from 4 continents and 11 families, and show that
our model yields comparable or better results than state-of-the-art, zero-shot
cross-lingual transfer methods. Moreover, we demonstrate that approximate
Bayesian model averaging results in smoother predictive distributions, whose
entropy inversely correlates with accuracy. Hence, the proposed framework also
offers robust estimates of prediction uncertainty. Our code is located at
github.com/cambridgeltl/parameter-factorizatio
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, V玫ro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Ash谩ninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems' predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems' performance on previously unseen lemmas.Peer reviewe
UniMorph 4.0:Universal Morphology
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet