Papers with morphological inflection
Resisting the Lure of the Skyline: Grounding Practices in Active Learning for Morphological Inflection (2024.acl-short)
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| Challenge: | Several approaches to active learning are available, including confidence-based, diversity-based and committee-based. |
| Approach: | They propose to use a baseline and a skyline to measure the accuracy of the unannotated sample pool. |
| Outcome: | The proposed model outperforms a random selection baseline and a skyline approach. |
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. |
Deep Active Learning for Morphophonological Processing (2023.acl-short)
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Seyed Morteza Mirbostani, Yasaman Boreshban, Salam Khalifa, SeyedAbolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing deep learning models for morphological processing require a large amount of annotated data. |
| Approach: | They propose a deep active learning method that uses only informative samples to reduce the need for annotated data. |
| Outcome: | The proposed method achieves the same results as the state-of-the-art model on Egyptian Arabic with only about 30% of annotated data. |
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings (N18-1)
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| Challenge: | linguistic typology is the classification of languages according to their linguistic properties. |
| Approach: | They learn distributed language representations which can be used to predict typological properties on a massively multilingual scale. |
| Outcome: | The proposed model can predict typological properties on a massively multilingual scale. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
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| Challenge: | Recent advances in morphological inflection generation have limited resources . antonisa and colleagues present a battery of improvements to improve performance under low-resource conditions . |
| Approach: | They propose a two-step attention architecture for the inflection decoder that uses two-segments attention and a multi-single-syllabic attention architecture. |
| Outcome: | The proposed model outperforms the state-of-the-art in low-resource languages by 15 percentage points . the proposed model also shows that it can be used to model monolingual data hallucinations . |
Morphology-Aware Multi-Granularity Representation Learning for Agglutinative Languages (2026.acl-srw)
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| Challenge: | Existing methods for learning low-resource agglutinative languages are limited to word and phrase levels. |
| Approach: | They propose a morphology-aware gated multi-granularity pre-training framework for agglutinative languages . framework leverages morphological knowledge and integrates a word-level encoder to capture contextual semantics . |
| Outcome: | The proposed framework improves on Mongolian and Turkish agglutinative languages . it leverages morphological knowledge and integrates tagging and segmentation to build fine-grained representations . |
Local String Transduction as Sequence Labeling (C18-1)
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| Challenge: | String transduction and sequence labeling are often treated as separate entities and often give treatment to different problems in NLP. |
| Approach: | They propose to reduce string transduction to sequence labeling by using a finite-state technique that uses string transducing and sequence labelling. |
| Outcome: | The proposed method performs better than seq2seq models and yields state-of-the-art results in several cases. |
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. |
Sparse Sequence-to-Sequence Models (P19-1)
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| Challenge: | Sequence-to-sequence models are dense and assigning nonzero probability to implausible outputs. |
| Approach: | They propose a new family of -entmax transformations that includes softmax and sparsemax as particular cases and is sparser for any > 1 . they provide fast algorithms to evaluate these transformations and their gradients, which scale well for large vocabulary sizes. |
| Outcome: | The proposed models are able to produce sparse alignments and assign nonzero probability to short list of plausible outputs, sometimes rendering beam search exact. |
Exact Hard Monotonic Attention for Character-Level Transduction (P19-1)
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| Challenge: | Neural sequence-to-sequence models with soft attention outperform monotonic models . current dominant method is the neural sequenceto-Sequency model with soft focus . |
| Approach: | They develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. |
| Outcome: | The proposed model achieves state-of-the-art on grapheme-to-phoneme conversion and morphological inflection generation. |
Applying the Transformer to Character-level Transduction (2021.eacl-main)
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| Challenge: | morphological inflection generation and historical text normalization tasks are character-level tasks that outperform recurrent models. |
| Approach: | They propose a technique to handle feature-guided character-level transduction that further improves performance. |
| Outcome: | The transformer outperforms recurrent models on morphological inflection and historical text normalization tasks. |
Pipeline Signed Japanese Translation Focusing on a Post-positional Particle Complement and Conjugation in a Low-resource Setting (2021.findings-acl)
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| Challenge: | a pipeline translation method is proposed to take advantage of the similarities and differences between sign language and spoken language. |
| Approach: | They propose a pipeline translation method that takes advantage of similarities between spoken and spoken Japanese . they map glosses to spoken language words and train them using a monolingual Japanese corpus . |
| Outcome: | The proposed method performs robustly on the low-resource corpus and is +4.4/+4.9 points above baseline. |
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. |
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules (2021.eacl-main)
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| Challenge: | Neural models for morphological inflection have recently attained very high results, but their interpretation remains challenging. |
| Approach: | They propose a linguistically-motivated variant to the encoder-decoder model with attention that incorporates a character-level cross-attention mechanism and a self-attention module over substrings of the input. |
| Outcome: | The proposed model performs well on three typologically-different languages and is highly interpretable. |
Surface Realisation Using Full Delexicalisation (D19-1)
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| Challenge: | Existing approaches to surface realisation model word ordering, morphological inflection and contraction generation are evaluated on 10 languages covered by the SR'18 shared task. |
| Approach: | They propose a modular approach which models each of these components separately and an analysis of the differences in word ordering performance across languages. |
| Outcome: | The proposed model is compared with existing models on 10 languages covered by the SR'18 shared task. |
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 . |
On Biasing Transformer Attention Towards Monotonicity (2021.naacl-main)
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| Challenge: | Existing work has focused on learning monotonic attention behavior via specialized attention functions or pretraining. |
| Approach: | They introduce a monotonicity loss function compatible with standard attention mechanisms and test it on sequence-to-sequence tasks. |
| Outcome: | The proposed monotonicity loss function can achieve largely monotonic behavior on grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization tasks. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
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| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
Do RNN States Encode Abstract Phonological Alternations? (2021.naacl-main)
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| Challenge: | Sequence-to-sequence models have been successful in word formation tasks, but the opacity of the models makes it difficult to determine whether complex generalizations are learned or whether there is some level of generalization across related sound changes. |
| Approach: | They propose to train character-based sequence-to-sequence models for inflection of Finnish nouns into the genitive case, an inflation type which is encoded in the hidden states of an LSTM encoderdecoder trained to perform word infference. |
| Outcome: | The proposed models encode 17 different consonant gradation processes in a handful of dimensions in the RNN. |
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. |
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. |
Getting The Most Out of Your Training Data: Exploring Unsupervised Tasks for Morphological Inflection (2024.emnlp-main)
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| Challenge: | Pre-trained transformers have been shown to be effective in many natural language tasks, but are under-explored for character-level sequence to sequence tasks. |
| Approach: | They propose to use pre-trained transformers for character-level morphological inflection in several languages to train models for unsupervised tasks. |
| Outcome: | The proposed model outperforms the best two shared tasks on morphological inflection and graphemeto-phoneme conversion benchmarks. |
OOVs in the Spotlight: How to Inflect Them? (2024.lrec-main)
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| Challenge: | Inflection is a process of word formation in which a base word form (lemma) is modified to express grammatical categories. |
| Approach: | They develop a retrograde model and two sequence-to-sequence models based on LSTM and Transformer. |
| Outcome: | The proposed systems outperform the existing systems on 9 out of 16 languages in the OOV evaluation. |
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives (2025.acl-long)
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| Challenge: | Rapid progress in natural language processing (NLP) has largely been driven by training transformer models on massive amounts of unlabeled data, but such large datasets are scarce for many of the world's languages. |
| Approach: | They propose to train encoder-decoder transformers for 19 languages and 13 auxiliary objectives on massive amounts of unlabeled data. |
| Outcome: | The proposed tasks outperform standard CMLM in character-level tasks when available data is limited. |
Neural Induction of Finite-State Transducers (2026.findings-acl)
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| Challenge: | Existing methods to construct finite-state transducers by hand are difficult and require domain knowledge and significant human effort. |
| Approach: | They propose a method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. |
| Outcome: | The proposed method outperforms classical transducer learning algorithms by up to 87% accuracy on held-out test sets. |
Emergent morpho-phonological representations in self-supervised speech models (2025.emnlp-main)
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| Challenge: | a recent study shows that self-supervised speech models do not represent phonological and morphological phenomena in frequent English noun and verb inflections. |
| Approach: | They study how S3Ms represent phonological and morphological phenomena in English . they propose alternative representational strategies that may support human spoken word recognition . |
| Outcome: | a new study shows that S3M models can represent phonological and morphological phenomena in English . the models can be trained to recognize spoken words in naturalistic, noisy environments . |