material ID: tamil

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Extending Kimmo’s Two-Level Model of Morphology

Authors: nan

Publication: TIPSTER (1993)

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This paper describes the problems faced while us- ing Kimmo's two-level model to describe certain Indian languages such as Tamil and Hindi. The two-level model is shown to be descriptively …...

35450

Sinhala-Tamil Machine Translation: Towards better Translation Quality

Authors: nan

Publication: TIPSTER (2014)

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Statistical Machine Translation (SMT) is a well-known and well established data- driven approach used for language trans- lation. The focus of this work is to de- velop a statistical machine …...

28526

Towards Semi-Automatic Generation of Proposition Banks for Low-Resource Languages

Authors: nan

Publication: TIPSTER (2016)

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Annotation projection based on parallel cor- pora has shown great promise in inexpensively creating Proposition Banks for languages for which high-quality parallel corpora and syn- tactic parsers are available. In …...

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Graph Based Semi-Supervised Learning Approach for Tamil POS tagging

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Publication: TIPSTER (2018)

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Parts of Speech (POS) tagging is an important pre-requisite for various Natural Language Processing tasks. POS tagging is rather challenging for morphologically rich languages such as Tamil. Being low-resourced, Tamil …...

17396

Resolving Pronouns for a Resource-Poor Language, Malayalam Using Resource-Rich Language, Tamil.

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Publication: TIPSTER (2019)

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AbstractIn this paper we give in detail how a resource rich language can be used for resolving pronouns for a less resource language. The source language, which is resource rich …...

19744

A Multilingual Parallel Corpora Collection Effort for Indian Languages

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe present sentence aligned parallel corpora across 10 Indian Languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English - many of which are categorized as …...

14615

Challenge Dataset of Cognates and False Friend Pairs from Indian Languages

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Publication: TIPSTER (2020)

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AbstractCognates are present in multiple variants of the same text across different languages (e.g., “hund” in German and “hound” in the English language mean “dog”). They pose a challenge to …...

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HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

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Publication: TIPSTER (2021)

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AbstractBack-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT …...

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HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation

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Publication: TIPSTER (2021)

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Understanding linguistics and morphology of resource-scarce code-mixed texts remains a key challenge in text processing. Although word embedding comes in handy to support downstream tasks for low-resource languages, there are …...

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Improving Translation Quality for Low-Resource Inuktitut with Various Preprocessing Techniques

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AbstractNeural machine translation has been shown to outperform all other machine translation paradigms when trained in a high-resource setting. However, it still performs poorly when dealing with low-resource languages, for …...

20066