Papers with Morphology
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. |
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization (D19-63)
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| Challenge: | Existing linearization methods for shallow surface realization tasks are not available for all languages. |
| Approach: | They propose a system that implements morphological inflection with a baseline linearizer for a shallow surface realization task. |
| Outcome: | The proposed system is competitive across languages, but poor on longer sentences. |
Juman++: A Morphological Analysis Toolkit for Scriptio Continua (D18-2)
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| Challenge: | a morphological analyzer is useful for languages without natural word boundaries, but it is difficult to improve it without creating costly annotations. |
| Approach: | They propose a toolkit for developing morphological analyzers for languages without natural word boundaries using lattices and neural nets. |
| Outcome: | The proposed morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. |
Neural Modeling for Named Entities and Morphology (NEMO2) (2021.tacl-1)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. |
| Approach: | They develop a morphologically rich-and-ambiguous language with a token-level and morpheme-level NER annotation framework to address Named Entity Recognition (NER) a novel hybrid architecture precedes and prunes morphology and outperforms the standard pipeline for Hebrew NER and Hebrew morphologies. |
| Outcome: | The proposed architecture outperforms the standard pipeline for Hebrew NER and Hebrew morphological decomposition tasks. |
Lost and Found: Computational Quality Assurance of Crowdsourced Knowledge on Morphological Defectivity in Wiktionary (2025.acl-srw)
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| Challenge: | a recent study shows that wikis are not reliable for linguistic knowledge of defects in understudied languages. |
| Approach: | They customize a neural morphological analyzer to annotate Latin and Italian corpora . they validated morphology using crowd-sourced data from Wiktionary to find defects . |
| Outcome: | The proposed algorithm annotates Latin and Italian corpora using crowd-sourced data . results show that 7% of Latin lemmata listed as defective show strong corpus evidence of being non-defective. |
LaVA – Latvian Language Learner corpus (2022.lrec-1)
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| Challenge: | a corpus of 1015 essays from foreigners learning Latvian as a foreign language is available at http://www.korpuss.lv/id/LaVA. |
| Approach: | They propose to create a Latvian Language Learner Corpus (LaVA) which contains 1015 essays from Latvian students with different language backgrounds. |
| Outcome: | The LaVA corpus contains 1015 essays from foreigners studying at Latvian higher education institutions and reaching the A1 (possibly A2) Latvian language proficiency level. |
Tools for The Production of Analogical Grids and a Resource of N-gram Analogical Grids in 11 Languages (L18-1)
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| Challenge: | a Python module implements several previously presented algorithms to build analogical grids from words contained in a corpus. |
| Approach: | They propose to release a Python module which implements several previously presented algorithms to build analogical grids from words contained in a corpus. |
| Outcome: | The tools were built on vocabularies contained in 1,000 lines of the 11 different language versions of the Europarl corpus v.3 and are language-independent, allowing their use with any language and any writing system. |
A Distributional and Orthographic Aggregation Model for English Derivational Morphology (P18-1)
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| Challenge: | Existing approaches to derived word generation model derivational morphology to generate words with particular semantics are not effective. |
| Approach: | They propose a novel aggregation model that learns derivational transformations as orthographic functions and as functions in distributional word embedding space. |
| Outcome: | The proposed model learns to choose between the hypothesis of each system and the hypothesis from the model. |
Contextualization of Morphological Inflection (N19-1)
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| Challenge: | In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. |
| Approach: | They propose a task that requires morphological features to be inferred from sentential context . they propose morphology-based models that explicitly reconstruct morphologic features before predicting inflected forms . |
| Outcome: | The proposed model is able to predict inflected sentences without relying on morphological annotations. |
An Investigation of Noise in Morphological Inflection (2023.findings-acl)
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| Challenge: | Neural morphological inflection systems can be used for languages with very little supervised data, but are often less likely to have clean, goldstandard data. |
| Approach: | They propose an error taxonomy and annotation pipeline for inflection training data and propose a character-level masked language modeling (CMLM) pretraining objective. |
| Outcome: | The proposed pipeline is based on error taxonomy and annotation pipelines for unsupervised morphological paradigm completion. |
Interactive Word Completion for Plains Cree (2022.acl-long)
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| Challenge: | a tool that helps users incrementally build complex words is being developed in morphologically complex languages. |
| Approach: | They propose a finite state approach which maps prefixes in a language to completions up to the next morpheme boundary for incremental building of complex words. |
| Outcome: | The proposed approach shows portability to a larger, more complete morphological transducer. |
Computational Modeling of Affixoid Behavior in Chinese Morphology (2020.coling-main)
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| Challenge: | affixoid behavior in Mandarin Chinese is unclear due to polysemy and diachronic dynamics. |
| Approach: | They propose to use three quantitative features to model affixoid behavior in Mandarin Chinese to determine its status. |
| Outcome: | The proposed model shows that there are no clear criteria that can be used to identify an affix’s status in an isolating language like Mandarin Chinese. |
A Computational Architecture for the Morphology of Upper Tanana (L18-1)
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| Challenge: | a computational model of Upper Tanana is described to model the Dene language . the model uses lexical-inflectional verb classes to predict possible derivations and their morphological behavior. |
| Approach: | They propose a computational model of Upper Tanana, a highly endangered Dene language . the model parses and generates inflected Upper Tanans and uses a lexical-inflectional verb system to predict possible derivations and their morphological behavior. |
| Outcome: | The proposed model parses and generates inflected Upper Tanana verb forms . it also uses the language's verb theme category system to predict possible derivations and their morphological behavior . |
A Framework for Bidirectional Decoding: Case Study in Morphological Inflection (2023.findings-emnlp)
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| Challenge: | Existing encoder-decoders that generate sequences from left to right are prone to errors due to the "snowballing" effect. |
| Approach: | They propose a transformer-based encoder-decoder framework that produces sequences from the "outside-in" they argue that this approach is more principled than prior bidirectional decoders . |
| Outcome: | The proposed model beats the current system by over 4.7 and 2.7 points in accuracy on 2022 and 2023 tasks. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |
Glawinette: a Linguistically Motivated Derivational Description of French Acquired from GLAWI (2020.lrec-1)
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| Challenge: | Glawinette is a derivational lexicon of French that will be used to feed Démonette, a large coverage morphological database. |
| Approach: | They propose to create a derivational lexicon of French that could be used to feed Démonette, a large coverage morphological database. |
| Outcome: | The proposed lexicon could be fed into Démonette, a large coverage morphological database. |
Multilingual Sequence-to-Sequence Models for Hebrew NLP (2023.findings-acl)
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| Challenge: | Recent work on pretrained language models for Hebrew is under-parameterized and under-trained . previous work on pretraining Hebrew LMs focused on encoder-only architectures . |
| Approach: | They propose to use sequence-to-sequence generative architectures to train large LMs in morphologically rich languages such as Hebrew. |
| Outcome: | The proposed model improves on all existing Hebrew NLP benchmarks. |
Eeny, meeny, miny, moe. How to choose data for morphological inflection. (2022.emnlp-main)
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| Challenge: | Data scarcity is a major bottleneck for many natural language processing tasks . active learning aims to reduce the cost of data annotation by selecting the most informative examples to label. |
| Approach: | They propose to use oracle experiments to select data that is most informative for the model. |
| Outcome: | The proposed sampling strategies show that they improve on the oracle experiment and the 10-cycle iteration using Natügu as a case study. |
Linguistic Compression in Single-Sentence Human-Written Summaries (2023.findings-emnlp)
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| Challenge: | Summarizing texts involves significant cognitive efforts to compress information. |
| Approach: | They analyze linguistic patterns in human-written single-sentence summaries to find that they are generally written with morphological expansion, increased lexical diversity, and similar positional arrangements of specific words . |
| Outcome: | The results show that human summarizers are written with morphological expansion, increased lexical diversity, and similar positional arrangements of specific words compared to the source across different genres. |
Preliminary Results on the Evaluation of Computational Tools for the Analysis of Quechua and Aymara (2022.lrec-1)
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| Challenge: | morphological analyzers for Quechua and Aymara have been evaluated for their performance . only a minority of these languages have been provided with adequate computational resources . |
| Approach: | They evaluate existing morphological analyzers for Quechua and Aymara . they also examine how they handle other individual languages of the macrolanguage . |
| Outcome: | The proposed tools perform well in Quechua and Aymara, and they can handle other languages. |
Bootstrapping Techniques for Polysynthetic Morphological Analysis (2020.acl-main)
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| Challenge: | Polysynthetic languages have exceptionally large and sparse vocabularies due to the number of morpheme slots and combinations in a word. |
| Approach: | They propose linguistically-informed approaches for bootstrapping a neural morphological analyzer . they use a finite state transducer to train an encoder-decoder model . |
| Outcome: | The proposed method improves on a polysynthetic language's model by "hallucinating" missing linguistic structure and resampling from a Zipf distribution to simulate a more natural distribution of morphemes. |
TallVocabL2Fi: A Tall Dataset of 15 Finnish L2 Learners’ Vocabulary (2022.lrec-1)
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| Challenge: | Existing work on second language knowledge has focused on the knowledge of small numbers of words, often geared towards measuring vocabulary size. |
| Approach: | They propose a “tall” word knowledge response dataset containing information about a few learners’ knowledge of many words. |
| Outcome: | The proposed dataset is based on a self-rating test and translation test and is compared with previous comparable datasets. |
Learning Bidirectional Morphological Inflection like Humans (2024.lrec-main)
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| Challenge: | Recent research has focused on whether neural models can acquire morphological inflection like humans. |
| Approach: | They propose to use a recurrent neural network with attention and the transformer to train a symbolic model under a human-like learning environment to evaluate their models. |
| Outcome: | The proposed models did not accurately inflect verbs in the same manner as humans in terms of morphological inflection direction. |
Qabas: An Open-Source Arabic Lexicographic Database (2024.lrec-main)
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| Challenge: | Qabas is an open-source Arabic lexicon designed for NLP applications. |
| Approach: | They propose to link lemmas from 110 lexicons into a morphologically annotated Arabic lexicoma. |
| Outcome: | Qabas lexical entries (lemmas) are assembled by linking lemmas from 110 lexicons. |
UzbekVerbDetection: Rule-based Detection of Verbs in Uzbek Texts (2024.lrec-main)
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| Challenge: | Verb detection is a fundamental task in natural language processing that involves identifying the action or state expressed by a verb in a sentence. |
| Approach: | They propose a rule-based approach for verb detection in Uzbek texts based on affixes/suffixed rules. |
| Outcome: | The proposed method outperforms existing methods on a dataset of Uzbek texts and has an F1 score of 0.97. |