Papers with lemmatization
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus (2022.lrec-1)
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| Challenge: | ZAEBUC is an annotated Arabic-English bilingual writer corpus . it is a corpus of short essays written by first-year university students . |
| Approach: | They propose to use a standard Arabic-English bilingual writer corpus to match comparable texts written by the same writer on different occasions. |
| Outcome: | The ZAEBUC corpus is an annotated Arabic-English bilingual writer corpus by first-year university students at Zayed University in the United Arab Emirates. |
DadmaTools: Natural Language Processing Toolkit for Persian Language (2022.naacl-demo)
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| Challenge: | Existing tools for Persian language processing are based on conventional non-neural models and do not take full advantage of the latest developments. |
| Approach: | They propose to use a Python neural pipeline for Persian text processing tasks . they use 'parsBERT' to fine-tune the Python pipeline using the PerDT dataset . |
| Outcome: | The proposed toolkit can achieve state-of-the-art performance on multiple NLP tasks. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
Improving Lemmatization of Non-Standard Languages with Joint Learning (N19-1)
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| Challenge: | Lemmatization is a task of mapping a token to its corresponding dictionary head-form to abstract away from orthographic and inflectional variation. |
| Approach: | They propose to improve lemmatization performance on non-standard historical languages . they propose an Encoder-Decoder architecture which enriches with sentence information . |
| Outcome: | The proposed model does not require POS or morphological annotations, which are not always available for historical corpora. |
A Simple Joint Model for Improved Contextual Neural Lemmatization (N19-1)
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| Challenge: | False positive: a core NLP task of lemmatization seeks to map multiple forms of English verbs to a canonical one, known as the lemma. |
| Approach: | They propose a joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
| Outcome: | The proposed model achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
Imitation Learning for Neural Morphological String Transduction (D18-1)
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| Challenge: | Recent studies have shown that neural transition-based models can be used for morphological tasks such as inflection generation and lemmatization without a character aligner or warm start. |
| Approach: | They propose to use imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. |
| Outcome: | The proposed model eliminates the need for a character aligner or warm start and achieves state-of-the-art performance on several datasets. |
A Resource for Studying Chatino Verbal Morphology (2020.lrec-1)
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| Challenge: | a recent study focused on the verbal inflectional morphology of a mesoamerican language, San Juan Quiahije Chatino . low-resource endangered languages are left behind, while high-resourced ones are left out . a new approach to document endangered languages is needed to ensure language use continues . |
| Approach: | They present a resource focusing on the verbal inflectional morphology of a mesoamerican language . they use morphological inflection tables and baseline results to assess the language's morphologies . |
| Outcome: | The proposed resource focuses on the verbal inflectional morphology of a mesoamerican language . the authors show that the results are consistent with the current state of the art in the field . |
Moving TIGER beyond Sentence-Level (L18-1)
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| Challenge: | TIGER 2.2-doc is a new set of annotations for the German TIger corpus. |
| Approach: | They propose a new set of annotations for the German TIGER corpus . they introduce new document-level annotations: authors and their gender. |
| Outcome: | The new annotations improve the TIGER corpus and its structure and authors and gender. |
Developing New Linguistic Resources and Tools for the Galician Language (L18-1)
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| Challenge: | Existing resources and tools for the Galician language are lacking for other less-resourced languages, such as statistical tools for lemmatization and Named Entity Recognition. |
| Approach: | They propose to develop a manually revised corpus for POS tagging and lemmatization, and a new manually annotated corpus to train existing statistical tools for the Galician language. |
| Outcome: | The proposed resources include a new corpus for POS tagging and lemmatization, and a manually annotated corpus to handle Named Entity recognition. |
Better Together: Modern Methods Plus Traditional Thinking in NP Alignment (2020.lrec-1)
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| Challenge: | a recent study shows that end-to-end systems are not structurally free. |
| Approach: | They propose to use dictionary- and word vector-based baselines to align NPs in the bitext . they argue that alignment of NP's in MT can be improved by using old-fashioned methods . |
| Outcome: | a new study shows that alignment of NPs in the bitext is relevant even in an end-to-end paradigm . the proposed system can be improved by bringing in old-fashioned methods, the authors argue . |
Parser combinators for Tigrinya and Oromo morphology (L18-1)
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Patrick Littell, Tom McCoy, Na-Rae Han, Shruti Rijhwani, Zaid Sheikh, David Mortensen, Teruko Mitamura, Lori Levin
| Challenge: | morphological parsers for two Afroasiatic languages are developed using a parser-combinator paradigm . the paradigm allows rapid development and ease of integration with other systems, but at a cost of non-optimal theoretical efficiency. |
| Approach: | They propose a rule-based morphological parser paradigm for Tigrinya and Oromo languages . they use a parsers-combinator paradigm instead of a finite-state paradigm . |
| Outcome: | The proposed paradigm allows rapid development and ease of integration with other systems, but at cost of non-optimal theoretical efficiency. |
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks (2024.findings-emnlp)
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| Challenge: | Morphologically rich languages are notoriously challenging to process for downstream NLP applications. |
| Approach: | They propose a pretrained model for NLP applications involving the morphologically rich language Sanskrit that outperforms previous models by a considerable margin. |
| Outcome: | The proposed model outperforms tokenized models on established Sanskrit word segmentation tasks and matches the current best lexicon-based model. |
Exploring Large Language Models for Classical Philology (2023.acl-long)
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| Challenge: | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. |
| Approach: | They propose to use encoder-only and encoder decoder architectures to create four models for Ancient Greek that vary along two dimensions for tasks of interest for Classical languages. |
| Outcome: | The proposed models improve on existing models of Ancient Greek and Latin and provide a large pre-training corpus for Ancient Greek to support the creation of a larger, comparable model zoo for Classical Philology. |
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024.lrec-main)
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| Challenge: | a corpus of multilingual Arabic-English speech is presented in a new paper . a major bottleneck is the lack of data needed for training NLP models . |
| Approach: | They propose a multilingual multidialectal Arabic-English speech corpus with a set of guidelines for automatic speech recognition. |
| Outcome: | The proposed corpus includes two languages with Arabic and English spoken in multiple variants and Arabic and Arabic with various accents. |