Evaluating Inflectional Complexity Crosslinguistically: a Processing Perspective (L18-1)
Copied to clipboard
| Challenge: | a cognitively motivated method for evaluating the inflectional complexity of a language is proposed . authors argue that some languages are inflectionally more complex than others . |
| Approach: | They propose a cognitively motivated method for evaluating inflectional complexity of a language . they use a recurrent self-organising neural network to learn "raw" inflected word forms . |
| Outcome: | The proposed method is independent of meta-linguistic issues and language-specific typological aspects. |
Similar Papers
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection (2022.emnlp-main)
Copied to clipboard
| Challenge: | Neural networks are at the center of a debate about human behavior in inflectional morphology. |
| Approach: | They measure correlation between human judgments and neural network probabilities for unknown word inflections. |
| Outcome: | The proposed model for morphological inflections correlates best with human wug ratings, but not with humans. |
Exploring Linguistic Probes for Morphological Inflection (2023.emnlp-main)
Copied to clipboard
| Challenge: | morphological inflection models typically employ language-independent data splitting algorithms. |
| Approach: | They propose language-specific probes to test aspects of morphological generalization . they use three morphology-distinct languages to test their generalization abilities . |
| Outcome: | The proposed language-specific probes are used to test morphological generalization abilities on three distinct languages. |
Morphological Inflection: A Reality Check (2023.acl-long)
Copied to clipboard
| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
What Kind of Language Is Hard to Language-Model? (P19-1)
Copied to clipboard
| Challenge: | a recent study suggests that language models perform poorly across languages. |
| Approach: | They propose a model that fits a paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at least-pairwise parallel corpora. |
| Outcome: | The proposed model is able to handle missing data and is aware of inter-sentence variation. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
Copied to clipboard
| 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 . |
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Using morphosyntactic tools, we train and distribute tools for approximately one thousand languages. |
| Approach: | They train and distribute morphosyntactic tools for approximately one thousand languages. |
| Outcome: | The results show that the tools generalize well across rare and common forms alike. |
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation (C18-1)
Copied to clipboard
| Challenge: | In this survey, I consolidate and categorize the approaches, identifying similarities and differences in computation and data, and show trade-offs that have to be considered. |
| Approach: | They consolidate and categorize approaches to incremental processing and show trade-offs that have to be considered. |
| Outcome: | The proposed approaches show that they have similarities and differences in computation and data and that they are not trivial. |
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)
Copied to clipboard
| Challenge: | Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. |
| Approach: | They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits. |
| Outcome: | The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models. |
A Morphology-Based Investigation of Positional Encodings (2024.emnlp-main)
Copied to clipboard
| Challenge: | Contemporary deep learning models handle languages with diverse morphology . morphological complexity of languages is closely linked with positional encodings . |
| Approach: | They propose to use positional encodings to integrate morphological complexity into deep learning models. |
| Outcome: | The proposed model improves on 22 languages and 5 downstream tasks. |