Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion

• URL: http://arxiv.org/abs/2301.05494v1
• Date: Fri, 13 Jan 2023 11:50:08 GMT
• Title: Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
• Authors: Ipek Baris Schlicht, Lucie Flek, Paolo Rosso
• Abstract summary: Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages.
• Score: 12.269362823116225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.

Related papers

• The Impact of Language Adapters in Cross-Lingual Transfer for NLU [0.8702432681310401]
  We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our results show that the effect of target-language adapters is highly inconsistent across tasks, languages and models. Removing the language adapter after training has only a weak negative effect, indicating that the language adapters do not have a strong impact on the predictions.
  arXiv  Detail & Related papers  (2024-01-31T20:07:43Z)
• Adapting the adapters for code-switching in multilingual ASR [10.316724084739892]
  Large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance. This formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. We propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network.
  arXiv  Detail & Related papers  (2023-10-11T12:15:24Z)
• Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech Recognition [28.416831396722106]
  We propose a language-universal adapter learning framework based on a pre-trained model for end-to-end multilingual automatic speech recognition. An online knowledge distillation is then used to enable the language-universal adapters to learn both language-specific and universal features. Compared to the conventional multilingual model, a 3.3% absolute error rate reduction is achieved.
  arXiv  Detail & Related papers  (2023-02-28T14:43:49Z)
• Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation [129.99918589405675]
  Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Multilingual fine-tuning improves performance on low-resource languages but requires modifying the entire model and can be prohibitively expensive. We propose training language-family adapters on top of mBART-50 to facilitate cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-09-30T05:02:42Z)
• xGQA: Cross-Lingual Visual Question Answering [100.35229218735938]
  xGQA is a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages. We propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual.
  arXiv  Detail & Related papers  (2021-09-13T15:58:21Z)
• Efficient Test Time Adapter Ensembling for Low-resource Language Varieties [115.12997212870962]
  Specialized language and task adapters have been proposed to facilitate cross-lingual transfer of multilingual pretrained models. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters.
  arXiv  Detail & Related papers  (2021-09-10T13:44:46Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.