On the Limitations of Sociodemographic Adaptation with Transformers

• URL: http://arxiv.org/abs/2208.01029v1
• Date: Mon, 1 Aug 2022 17:58:02 GMT
• Title: On the Limitations of Sociodemographic Adaptation with Transformers
• Authors: Chia-Chien Hung, Anne Lauscher, Dirk Hovy, Simone Paolo Ponzetto, Goran Glava\v{s}
• Abstract summary: Sociodemographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating specific sociodemographic factors can consistently improve performance for various NLP tasks. We use three common specialization methods proven effective for incorporating external knowledge into pretrained Transformers.
• Score: 34.768337465321395
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sociodemographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating specific sociodemographic factors can consistently improve performance for various NLP tasks in traditional NLP models. We investigate whether these previous findings still hold with state-of-the-art pretrained Transformers. We use three common specialization methods proven effective for incorporating external knowledge into pretrained Transformers (e.g., domain-specific or geographic knowledge). We adapt the language representations for the sociodemographic dimensions of gender and age, using continuous language modeling and dynamic multi-task learning for adaptation, where we couple language modeling with the prediction of a sociodemographic class. Our results when employing a multilingual model show substantial performance gains across four languages (English, German, French, and Danish). These findings are in line with the results of previous work and hold promise for successful sociodemographic specialization. However, controlling for confounding factors like domain and language shows that, while sociodemographic adaptation does improve downstream performance, the gains do not always solely stem from sociodemographic knowledge. Our results indicate that sociodemographic specialization, while very important, is still an unresolved problem in NLP.

Related papers

• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• SocioProbe: What, When, and Where Language Models Learn about Sociodemographics [31.040600510190732]
  We investigate the sociodemographic knowledge of pre-trained language models (PLMs) on multiple English data sets. Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs. Our overall results indicate that sociodemographic knowledge is still a major challenge for NLP.
  arXiv  Detail & Related papers  (2022-11-08T14:37:45Z)
• Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers [34.768337465321395]
  Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. We use three common specialization methods proven effective for incorporating external knowledge into pretrained Transformers. We adapt the language representations for the demographic dimensions of gender and age, using continuous language modeling and dynamic multi-task learning.
  arXiv  Detail & Related papers  (2022-10-13T21:16:27Z)
• Visual Comparison of Language Model Adaptation [55.92129223662381]
  adapters are lightweight alternatives for model adaptation. In this paper, we discuss several design and alternatives for interactive, comparative visual explanation methods. We show that, for instance, an adapter trained on the language debiasing task according to context-0 embeddings introduces a new type of bias.
  arXiv  Detail & Related papers  (2022-08-17T09:25:28Z)
• Detecting ESG topics using domain-specific language models and data augmentation approaches [3.3332986505989446]
  Natural language processing tasks in the financial domain remain challenging due to paucity of appropriately labelled data. Here, we investigate two approaches that may help to mitigate these issues. Firstly, we experiment with further language model pre-training using large amounts of in-domain data from business and financial news. We then apply augmentation approaches to increase the size of our dataset for model fine-tuning.
  arXiv  Detail & Related papers  (2020-10-16T11:20:07Z)
• Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
  A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size. We propose a fully compositional output embedding layer for language models. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
  arXiv  Detail & Related papers  (2020-09-24T07:21:14Z)

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.