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Found 39 papers, 14 papers with code
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe.
Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection
The paper describes the Flexica team’s submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection.
Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification
The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification.
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.
The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists.
Exploring Looping Effects in RNN-based Architectures
The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems.
Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service
The impact of machine translation (MT) on low-resource languages remains poorly understood.
Generating bilingual example sentences with large language models as lexicography assistants
Additionally, we explore the use of pre-trained language models for automated rating of examples, finding that sentence perplexity serves as a good proxy for typicality and intelligibility in higher-resourced languages.
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Inflection
Linguistic fieldwork is an important component in language documentation and preservation.
A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
Historical linguists have identified multiple forms of lexical semantic change.
Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (including ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.
Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language
Leveraging a novel corpus derived from a Mambai language manual and additional sentences translated by a native speaker, we examine the efficacy of few-shot LLM prompting for machine translation (MT) in this low-resource context.
Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation
On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency.
Predicting Human Translation Difficulty with Neural Machine Translation
Human translators linger on some words and phrases more than others, and predicting this variation is a step towards explaining the underlying cognitive processes.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.
Ranked #8 on
Morpheme Segmentaiton
on UniMorph 4.0
UniMorph 4.0: Universal Morphology
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.
SIGTYP 2021 Shared Task: Robust Spoken Language Identification
While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task.
Modelling Verbal Morphology in Nen
Nen verbal morphology is remarkably complex; a transitive verb can take up to 1, 740 unique forms.
SIGTYP 2020 Shared Task: Prediction of Typological Features
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
UniMorph 3.0: Universal Morphology
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.
Weird Inflects but OK: Making Sense of Morphological Generation Errors
We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection.
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages.
Evaluation of Semantic Change of Harm-Related Concepts in Psychology
The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology.
Contextualization of Morphological Inflection
Critical to natural language generation is the production of correctly inflected text.
UniMorph 2.0: Universal Morphology
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task.
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression.
Paradigm Completion for Derivational Morphology
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task.
Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations
We present a quantitative analysis of human word association pairs and study the types of relations presented in the associations.
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms.
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation.
From Incremental Meaning to Semantic Unit (phrase by phrase)
This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10.
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision.
