Improving Morphological Inflection for Out-of-Vocabulary Words: A Comparative Study of Retrograde, LSTM, and Transformer Models

Core Concepts

Developing effective systems for morphological inflection of out-of-vocabulary (OOV) words, a challenging task where state-of-the-art models often underperform.

Abstract

The paper focuses on the task of morphological inflection in out-of-vocabulary (OOV) conditions, an understudied area where current state-of-the-art systems usually perform poorly. The authors developed three different data-driven systems to address this challenge:

A retrograde model that finds the most similar word in a database and inflects the input lemma accordingly.
An LSTM-based sequence-to-sequence model.
A Transformer-based sequence-to-sequence model.

To enable rigorous evaluation in OOV conditions, the authors created the Czech OOV Inflection Dataset, a lemma-disjoint split of a large Czech morphological dictionary, as well as a manually annotated dataset of Czech neologisms.
On the test-MorfFlex dataset, the Transformer-based model achieved the best performance, outperforming the other models and baselines. However, on the real-world test-neologisms dataset, the retrograde model outperformed the neural models.
The authors also evaluated their seq2seq models on the SIGMORPHON 2022 shared task data for 16 languages, where they achieved state-of-the-art results in 9 out of the 16 languages in the OOV evaluation condition.
Finally, the authors release the Czech OOV Inflection Dataset and a ready-to-use Python library with their inflection models, contributing valuable resources for further research on this task.

Stats

The Czech OOV Inflection Dataset contains over 5 million lemma-tag-form entries, with lemma-disjoint train, dev and test splits.
The manually annotated test-neologisms dataset contains 101 Czech neologisms with their inflected forms.
The SIGMORPHON 2022 shared task data includes datasets for 16 languages, with around 2,000 lemma-tag-form entries per language in the large data condition.

Quotes

"To provide a consistent benchmark for inflection in OOV context, we release the Czech OOV Inflection Dataset1 for rigorous evaluation, with a lemma-disjoint train-dev-test split of the pre-existing large morphological dictionary MorfFlex (Hajič et al., 2020)."
"In the standard OOV conditions, Transformer achieves the best results, with increasing performance in ensemble with LSTM, the retrograde model and SIGMORPHON baselines. On the real-world OOV dataset of neologisms, the retrograde model outperforms all neural models."
"Finally, our seq2seq models achieve state-of-the-art results in 9 out of 16 languages from SIGMORPHON 2022 shared task data in the OOV evaluation (feature overlap) in the large data condition."

Key Insights Distilled From

OOVs in the Spotlight: How to Inflect them?

by Tomá... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08974.pdf

Deeper Inquiries

How do the performance differences between the models on the test-MorfFlex and test-neologisms datasets reflect the strengths and limitations of each approach?

The performance differences between the models on the test-MorfFlex and test-neologisms datasets highlight the strengths and limitations of each approach in handling out-of-vocabulary (OOV) conditions. On the test-MorfFlex dataset, the Transformer model achieved the best results, showcasing its ability to effectively inflect words when trained on a large dataset with known lemmas. This indicates that the Transformer architecture is well-suited for tasks where training data is plentiful and covers a wide range of vocabulary.
In contrast, on the test-neologisms dataset, the retrograde model outperformed the neural models. The retrograde model's success can be attributed to its reliance on lexicographical similarity and the ability to find the most similar word in the database based on the longest common suffix. This approach proved effective in inflecting neologisms, which are new words not present in the training data, by leveraging similarities in word endings.
The neural models, such as LSTM and Transformer, struggled more on the test-neologisms dataset due to the lack of exposure to these new words during training. While neural models excel in learning complex patterns and generalizing well, they may face challenges with OOV words that deviate significantly from the training data. This highlights the importance of dataset diversity and the need for models to handle unseen inputs effectively.

What are the potential reasons for the retrograde model's superior performance on the real-world neologisms dataset compared to the neural models?

The retrograde model's superior performance on the real-world neologisms dataset compared to the neural models can be attributed to several factors:

Lexicographical Similarity: The retrograde model's approach of finding the most similar word based on the longest common suffix is particularly effective for neologisms that may share similarities with existing words in the database. This method leverages the linguistic structure of the language, where words with similar endings often inflect in a similar manner.

Simplicity and Specificity: The retrograde model's straightforward algorithm is tailored to the specific task of inflecting words based on suffix similarity. This simplicity allows it to handle OOV words effectively without the need for extensive training on diverse vocabulary.

Language Dependency: The retrograde model's success on Czech neologisms reflects its strong language dependency. Czech, being a morphologically rich language, benefits from the retrograde model's focus on suffix-based inflection, which aligns well with the language's inflectional patterns.

Handling Compounds and Blends: The retrograde model's ability to handle compounds, blends, and words derived by prefixing effectively contributes to its performance on neologisms, which often involve novel word formations. By ignoring prefixes and focusing on suffixes, the model can accurately inflect these complex words.

How could the insights from this study on morphological inflection of OOV words be applied to improve natural language generation systems in other domains?

The insights from this study on morphological inflection of OOV words can be valuable for enhancing natural language generation systems in various domains:

OOV Handling: Implementing strategies like the retrograde model's approach of leveraging lexicographical similarity can improve OOV word handling in natural language generation systems. By focusing on suffix similarities and known word structures, models can generate accurate inflections for unseen words.

Domain Adaptation: Applying the concept of dataset diversity and lemma-disjoint splits can help natural language generation systems adapt to new domains or languages. By training on a diverse range of vocabulary and ensuring coverage of OOV scenarios, models can generalize better to different linguistic contexts.

Ensemble Techniques: Utilizing ensemble methods, as demonstrated in this study, can enhance the robustness of natural language generation systems. By combining multiple models and leveraging their complementary strengths, systems can achieve higher accuracy and reliability in generating text.

Language-specific Approaches: Tailoring inflection models to the linguistic characteristics of specific languages, such as morphologically rich languages like Czech, can improve the performance of natural language generation systems in those languages. Understanding language-specific inflection patterns is crucial for accurate text generation.

By incorporating these insights and methodologies, natural language generation systems can enhance their ability to generate grammatically correct and contextually appropriate text, especially when faced with OOV words and complex linguistic structures.