Phonological Features for Morphological Inflection

Wiemerslage, Adam; Silfverberg, Miikka; Hulden, Mans

doi:10.18653/v1/w18-5818

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…Notably, there exist NLP approaches such as the document classification approach of showing that indeed shared character-level information can facilitate cross-lingual transfer, but limit their analysis to same-script languages only. Specific to the the morphological inflection task, (Hauer et al, 2019) use cognate projection to augment low-resource data, while (Wiemerslage et al, 2018) explore the inflection task using inputs in phonological space as well as bundles of phonological features from PanPhon , showing improvements for both settings. Our work, in contrast, focuses on better cross-lingual transfer, attempting to combine the phonological and the orthographic space.…”

Section: Introductionmentioning

confidence: 99%

Transliteration for Cross-Lingual Morphological Inflection

Murikinati¹,

Anastasopoulos²,

Neubig³

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Cross-lingual transfer between typologically related languages has been proven successful for the task of morphological inflection. However, if the languages do not share the same script, current methods yield more modest improvements. We explore the use of transliteration between related languages, as well as grapheme-to-phoneme conversion, as data preprocessing methods in order to alleviate this issue. We experimented with several diverse language pairs, finding that in most cases transliterating the transfer language data into the target one leads to accuracy improvements, even up to 9 percentage points. Converting both languages into a shared space like the International Phonetic Alphabet or the Latin alphabet is also beneficial, leading to improvements of up to 16 percentage points. 1

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Section: Introductionmentioning

confidence: 99%

Transliteration for Cross-Lingual Morphological Inflection

Murikinati¹,

Anastasopoulos²,

Neubig³

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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“…As such, depending on the opacity of a language's orthographic system, information about allophones, syllables, and other phonetically important structures is lost. Interestingly, this loss does not seem to impact neuralnetwork models of inflection (Wiemerslage et al, 2018), though the current model's rule-based approach likely suffers.…”

Section: Inflectingmentioning

confidence: 89%

DepDist: Surface realization via regex and learned dependency-distance tolerance

Dyer

2019

Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

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This paper describes a method of inflecting and linearizing a lemmatized dependency tree by: (1) determining a regular expression and substitution to describe each productive wordform rule; (2) learning the dependency distance tolerance for each head-dependent pair, resulting in an edge-weighted directed acyclic graph (DAG); and (3) topologically sorting the DAG into a surface realization based on edge weight. The method's output for 11 languages across 18 treebanks is competitive with the other submissions to the Second Multilingual Surface Realization Shared Task (SR'19).

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“…The SIGMORPHON 2019 shared task provided a type-level evaluation on 100 language pairs in 79 languages and a token-level evaluation on 107 treebanks in 66 languages, of systems for inflection and analysis. On task 1 (low-resource inflection with cross-lingual transfer), 14 systems were submitted, while on task 2 (lemmatization and morphological feature analysis), 16 systems were sub-7 Although some work suggests that working with IPA or phonological distinctive features in this context yields very similar results to working with graphemes (Wiemerslage et al, 2018). 8 This has been addressed by Jin and Kann (2017). mitted.…”

Section: Discussionmentioning

confidence: 99%

“…Although some work suggests that working with IPA or phonological distinctive features in this context yields very similar results to working with graphemes(Wiemerslage et al, 2018).8 This has been addressed byJin and Kann (2017).…”

mentioning

confidence: 99%

The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection

McCarthy¹,

Vylomova²,

Wu³

et al. 2019

Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

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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. The first task evolves past years' inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year's strong baselines or highly ranked systems from previous years' shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines. Data Data for Task 1Language pairs We presented data in 100 language pairs spanning 79 unique languages. Data for all but four languages (Basque, Kurmanji, Murrinhpatha, and Sorani) are extracted from English Wiktionary, a large multi-lingual crowdsourced dictionary with morphological paradigms

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Phonological Features for Morphological Inflection

Cited by 7 publications

References 15 publications

Transliteration for Cross-Lingual Morphological Inflection

Transliteration for Cross-Lingual Morphological Inflection

DepDist: Surface realization via regex and learned dependency-distance tolerance

The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection

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