| Challenge: | a Paradigm cell filling problem is a problem that asks how speakers of a language can reliably produce inflectional forms without ever witnessing them before. |
| Approach: | They implement novel neural models for the Paradigm Cell Filling Problem in morphology . they evaluate models on 18 data sets in 8 languages and implement them in a new dataset . |
| Outcome: | The proposed model performs comparable to previous work with less training data. |
Similar Papers
The Paradigm Discovery Problem (2020.acl-main)
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| Challenge: | a paradigm discovery problem is a task of learning an inflectional morphological system from unannotated sentences. |
| Approach: | They formalize the paradigm discovery problem and develop evaluation metrics for judging systems . they use word embeddings and string similarity to cluster forms by cell and by paradigm . |
| Outcome: | The proposed system suggests clustering by cell across different inflection classes is the most pressing challenge for future work. |
A Computational Model for the Linguistic Notion of Morphological Paradigm (C18-1)
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| Challenge: | In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms has been proposed as an efficient method to deduce the inflection of unseen word forms. |
| Approach: | They propose to generalize inflectional tables into more abstract paradigms by aligning the longest common subsequence found in an inflection table with the longest lexeme. |
| Outcome: | The proposed method matches linguist intuitions about what an inflectional paradigm is and can reconstruct missing inflections and generalize and group the witnessed patterns into a model of more abstract paradigmatic behavior of lexemes. |
Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers (2025.findings-acl)
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| Challenge: | A common generation task in morphology is morphological inflection, where a target form has to be generated from its corresponding lemma and feature tag. |
| Approach: | They propose to solve the Paradigm Cell Filling Problem (PCFP) by using encoder-decoder transformers to generate inflected verbs in Spanish. |
| Outcome: | The proposed model performs better on L-shaped verbs than regular verbs, but no consistent recency effects are observed. |
STable: Table Generation Framework for Encoder-Decoder Models (2024.eacl-long)
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Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Nowakowska, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek
| Challenge: | Existing approaches to infer text-to-table neural models are limited to raw text, but the proposed framework is capable of unifying a variety of problems involving natural language. |
| Approach: | They propose a framework for text-to-table neural models that utilizes a generalized sequential method that comprehends information from all cells in the table. |
| Outcome: | The proposed framework outperforms previous approaches on several challenging datasets and outperformed existing models by up to 15%. |
Unsupervised Morphological Paradigm Completion (2020.acl-main)
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| Challenge: | a task of generating morphological paradigms is a challenging unsupervised task for natural language processing systems . acuidados y acciones del idioma es a problem in linguistic annotators. |
| Approach: | They propose a task of unsupervised morphological paradigm completion using raw text and a lemma list. |
| Outcome: | The proposed system outperforms trivial baselines on 14 typologically diverse languages with ease and higher accuracy than minimally supervised systems. |
Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals (2020.acl-main)
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| Challenge: | Encoder-decoder models can be used to generalize to inflectional morphology and generalize new words, but they fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. |
| Approach: | They propose to use a dataset to collect data from German speakers to examine whether ED models can generalize the most frequently produced plural class. |
| Outcome: | The proposed model does not show human-like variability or ‘regular’ extension of other plural markers. |
Searching for Search Errors in Neural Morphological Inflection (2021.eacl-main)
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| Challenge: | Neural sequence-to-sequence models are the predominant choice for language generation tasks. |
| Approach: | They find that on word-level tasks, the empty string is often the global optimum . they suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
| Outcome: | The results suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
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. |
The Concordia NLG Surface Realizer at SRST 2019 (D19-63)
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| Challenge: | The goal of Natural Language Generation (NLG) is to produce natural texts given structured data. |
| Approach: | They propose a model for the shallow track of the 2019 NLG Surface Realization Shared Task . they divided the problem into two sub-problems: reordering and inflecting . |
| Outcome: | The proposed model reconstructs sentences whose word order and word inflections were removed. |
Minimal Supervision for Morphological Inflection (2021.emnlp-main)
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| Challenge: | Neural models for morphological reinflection tasks have proved to be extremely accurate given ample labeled data, yet labele d data may be slow and costly to obtain. |
| Approach: | They exploit orthographic and semantic regularities in morphological systems to exploit the orthographic regularities on their own to achieve respectable accuracy. |
| Outcome: | The bootstrapping method outperforms hallucination-based methods for morphological reinflection tasks. |