Evaluating morphological typology in zero-shot cross-lingual transfer (2021.acl-long)
Copied to clipboard
| Challenge: | morphological typology has been used to improve cross-lingual transfer . however, some language families and typologies consistently perform worse . |
| Approach: | They examine effects of morphological typology on zero-shot cross-lingual transfer . they perform part-of-speech tagging and sentiment analysis on 19 languages . |
| Outcome: | The proposed model improves on fusional and introflexive languages, but some language families and typologies perform worse. |
Similar Papers
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
| Approach: | They propose to use multilingual pre-trained models to model cross-lingual transfer in a selection of target languages. |
| Outcome: | The proposed model can be used to improve cross-lingual transfer performance in low-resource languages with no labeled training data. |
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. (2021.naacl-srw)
Copied to clipboard
| Challenge: | Negation resolution remains an acute and continuously researched question in Natural Language Processing. |
| Approach: | They propose to use multilingual pre-trained general representation models to detect negation scope in languages without annotated data. |
| Outcome: | The proposed model achieves token-level F1 score between English, Spanish, French, and Russian. |
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling (D18-1)
Copied to clipboard
| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
| Approach: | They propose to use a full-vocabulary setup to test the performance of language modeling (LM) on 50 typologically diverse languages. |
| Outcome: | The proposed language modeling task is based on a full vocabulary setup focused on word-level prediction on 50 typologically diverse languages. |
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. |
Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent advances in training multilingual models on large datasets have shown promising results in knowledge transfer across languages. |
| Approach: | They challenge the assumption that high zero-shot performance reflects high cross-lingual ability by introducing more challenging setups involving instances with multiple languages. |
| Outcome: | The proposed model can achieve high performance on multilingual benchmarks and on low-resource languages. |
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
Copied to clipboard
| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
Why do language models perform worse for morphologically complex languages? (2025.coling-main)
Copied to clipboard
| Challenge: | Language models perform differently across languages, a new study suggests . morphological typology may explain some of the performance differences, authors say . |
| Approach: | They propose to test morphological alignment of tokenizers, tokenization quality and disparities in dataset sizes and measurement to test this hypothesis. |
| Outcome: | The proposed model shows that fusional languages perform better than fusionative languages . the authors suggest that morphological typology may explain some of the performance differences . |
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing training data is limited for languages other than English, so is the performance of the developed parsers. |
| Approach: | They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available. |
| Outcome: | The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features. |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
Copied to clipboard
| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |