The study focuses on Pretrained Language Models (PLMs) and their ability to capture cross-lingual word sense knowledge. While PLMs can be finetuned for tasks such as translation and multilingual word sense disambiguation (WSD), they often struggle in a zero-shot setting. To address this issue, the authors introduce Contextual Word-Level Translation (C-WLT), an extension of word-level translation that prompts the model to translate a given word in context. The study investigates how well PLMs encode cross-lingual word sense knowledge with C-WLT and finds that larger models perform better at using context to improve WLT performance. Building on C-WLT, the authors propose a zero-shot approach for WSD, tested on 18 languages from the XL-WSD dataset. Their method outperforms fully supervised baselines on recall for many evaluation languages without additional training or finetuning. The study compares their approach to prior work on multilingual WSD using automatic metrics such as recall and Jaccard index. They found that ensembling English, Chinese, and Russian as target languages with English prompts achieved a balance between recall and Jaccard Index. Despite being performed zero-shot from a pretrained language model, their method achieves higher recall compared to prior works in 11 out of the 18 source languages, showing that translation-based approaches can identify correct sense labels as well or better than supervised methods. However, there are limitations to their approach such as relying on the availability of high quality translations and not considering polysemous words with multiple senses within one language. The authors recognize these limitations and suggest future research directions to address them and improve applicability. Overall, this study presents a first step towards leveraging cross-lingual knowledge inside PLMs for robust zero-shot reasoning in any language.
- The study focuses on Pretrained Language Models (PLMs) and their ability to capture cross-lingual word sense knowledge.
- PLMs can be finetuned for tasks such as translation and multilingual word sense disambiguation (WSD), but they often struggle in a zero-shot setting.
- The authors introduce Contextual Word-Level Translation (C-WLT), an extension of word-level translation that prompts the model to translate a given word in context, to address this issue.
- Larger models perform better at using context to improve WLT performance.
- The authors propose a zero-shot approach for WSD, tested on 18 languages from the XL-WSD dataset. Their method outperforms fully supervised baselines on recall for many evaluation languages without additional training or finetuning.
- The study compares their approach to prior work on multilingual WSD using automatic metrics such as recall and Jaccard index. They found that ensembling English, Chinese, and Russian as target languages with English prompts achieved a balance between recall and Jaccard Index.
- Despite being performed zero-shot from a pretrained language model, their method achieves higher recall compared to prior works in 11 out of the 18 source languages, showing that translation-based approaches can identify correct sense labels as well or better than supervised methods.
- Limitations of their approach include relying on the availability of high quality translations and not considering polysemous words with multiple senses within one language.
- Future research directions are suggested to address these limitations and improve applicability.
- Overall, this study presents a first step towards leveraging cross-lingual knowledge inside PLMs for robust zero-shot reasoning in any language.
The study is about how computers can understand words in different languages. They use something called Pretrained Language Models (PLMs) to help them. These models can be trained to translate and understand words, but sometimes they have trouble doing it without any training. The authors made a new way for the computer to understand words in context, which helps it work better without extra training. They tested their method on many different languages and found that it worked well.
Definitions
- Pretrained Language Models (PLMs): A type of computer program that has already been trained to understand language.
- Multilingual word sense disambiguation (WSD): Figuring out the meaning of a word when there are multiple possible meanings.
- Zero-shot setting: When a computer program is asked to do something without any specific training for that task.
- Contextual Word-Level Translation (C-WLT): A way of translating words that takes into account the sentence or paragraph they are in.
- Recall: How many correct answers a computer program gives compared to how many there actually are.
- Jaccard index: A way of measuring how similar two sets of things are.
- Ensembling: Combining multiple models together to get better results.
- Polysemous words: Words that have multiple meanings.
Exploring the Ability of Pretrained Language Models to Capture Cross-Lingual Word Sense Knowledge
Recent advances in natural language processing (NLP) have been driven by the development of pretrained language models (PLMs). PLMs are powerful tools for tasks such as translation and multilingual word sense disambiguation (WSD), but they often struggle in a zero-shot setting. To address this issue, researchers from the University of Edinburgh recently proposed Contextual Word-Level Translation (C-WLT), an extension of word-level translation that prompts the model to translate a given word in context. The study investigates how well PLMs encode cross-lingual word sense knowledge with C-WLT and finds that larger models perform better at using context to improve WLT performance.
Background on Multilingual WSD
Multilingual WSD is a task where words must be assigned their correct sense labels across languages. This can be done either through supervised methods or unsupervised methods such as machine translation, which relies on translating words into another language before assigning them sense labels. However, these approaches require additional training or finetuning and may not always yield accurate results due to errors in translations or lack of data for certain languages.
Contextual Word Level Translation
To overcome these issues, the authors propose C-WLT, an extension of word level translation that takes into account contextual information when translating words between languages. This approach allows the model to capture more nuanced meaning than traditional machine translation alone and improves accuracy when used for multilingual WSD tasks. The authors tested their method on 18 languages from the XL-WSD dataset and compared it against fully supervised baselines using automatic metrics such as recall and Jaccard index. They found that ensembling English, Chinese, and Russian as target languages with English prompts achieved a balance between recall and Jaccard Index while outperforming prior works in 11 out of 18 source languages without additional training or finetuning.
Limitations & Future Directions
Despite its promising results, there are still limitations to this approach such as relying on high quality translations between two languages and not considering polysemous words with multiple senses within one language. The authors recognize these limitations and suggest future research directions to address them including exploring other forms of cross lingual transfer learning techniques beyond C-WLT for improved performance on multilingual WSD tasks.
Conclusion
Overall, this study presents a first step towards leveraging cross linguistic knowledge inside PLMs for robust zero shot reasoning in any language without requiring additional training or finetuning data sets specific to each target language being evaluated upon . By introducing Contextual Word Level Translation (C - WLT), they were able to achieve higher recall compared to prior works in 11 out of 18 source languages showing that translation based approaches can identify correct sense labels as well or better than supervised methods .
