Evaluating Named Entity Recognition: Comparative Analysis of Mono- and Multilingual Transformer Models on Brazilian Corporate Earnings Call Transcriptions

• URL: http://arxiv.org/abs/2403.12212v1
• Date: Mon, 18 Mar 2024 19:53:56 GMT
• Title: Evaluating Named Entity Recognition: Comparative Analysis of Mono- and Multilingual Transformer Models on Brazilian Corporate Earnings Call Transcriptions
• Authors: Ramon Abilio, Guilherme Palermo Coelho, Ana Estela Antunes da Silva,
• Abstract summary: This study focuses on Portuguese-language texts extracted from earnings call transcriptions of Brazilian banks. By curating a comprehensive dataset comprising 384 transcriptions, we evaluate the performance of monolingual models trained on Portuguese. Our findings reveal that BERT-based models consistently outperform T5-based models.
• Score: 3.809702129519642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Named Entity Recognition (NER) is a Natural Language Processing technique for extracting information from textual documents. However, much of the existing research on NER has been centered around English-language documents, leaving a gap in the availability of datasets tailored to the financial domain in Portuguese. This study addresses the need for NER within the financial domain, focusing on Portuguese-language texts extracted from earnings call transcriptions of Brazilian banks. By curating a comprehensive dataset comprising 384 transcriptions and leveraging weak supervision techniques for annotation, we evaluate the performance of monolingual models trained on Portuguese (BERTimbau and PTT5) and multilingual models (mBERT and mT5). Notably, we introduce a novel approach that reframes the token classification task as a text generation problem, enabling fine-tuning and evaluation of T5 models. Following the fine-tuning of the models, we conduct an evaluation on the test dataset, employing performance and error metrics. Our findings reveal that BERT-based models consistently outperform T5-based models. Furthermore, while the multilingual models exhibit comparable macro F1-scores, BERTimbau demonstrates superior performance over PTT5. A manual analysis of sentences generated by PTT5 and mT5 unveils a degree of similarity ranging from 0.89 to 1.0, between the original and generated sentences. However, critical errors emerge as both models exhibit discrepancies, such as alterations to monetary and percentage values, underscoring the importance of accuracy and consistency in the financial domain. Despite these challenges, PTT5 and mT5 achieve impressive macro F1-scores of 98.52% and 98.85%, respectively, with our proposed approach. Furthermore, our study sheds light on notable disparities in memory and time consumption for inference across the models.

Related papers

• BERT vs GPT for financial engineering [0.0]
  The paper benchmarks several Transformer models to show how these models can judge sentiment from a news event. We find that fine-tuned BERT models outperform fine-tuned or vanilla GPT models on this task.
  arXiv  Detail & Related papers  (2024-04-24T11:30:04Z)
• A Text-to-Text Model for Multilingual Offensive Language Identification [19.23565690468299]
  This study presents the first pre-trained model with encoder-decoder architecture for offensive language identification with text-to-text transformers (T5) Our pre-trained T5 model outperforms other transformer-based models fine-tuned for offensive language detection, such as fBERT and HateBERT, in multiple English benchmarks. Following a similar approach, we also train the first multilingual pre-trained model for offensive language identification using mT5.
  arXiv  Detail & Related papers  (2023-12-06T09:37:27Z)
• On Robustness of Finetuned Transformer-based NLP Models [11.063628128069736]
  We characterize changes between pretrained and finetuned language model representations across layers using two metrics: CKA and STIR. GPT-2 representations are more robust than BERT and T5 across multiple types of input perturbations. This study provides valuable insights into perturbation-specific weaknesses of popular Transformer-based models.
  arXiv  Detail & Related papers  (2023-05-23T18:25:18Z)
• Transformer-based approaches to Sentiment Detection [55.41644538483948]
  We examined the performance of four different types of state-of-the-art transformer models for text classification. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions.
  arXiv  Detail & Related papers  (2023-03-13T17:12:03Z)
• Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation [9.736284584478032]
  We show the effectiveness of character-level modeling in translation, particularly in cases where fine-tuning data is limited. While evaluating the importance of source texts in driving model predictions, we highlight word-level patterns within ByT5. We conclude by assessing the efficiency tradeoff of byte models, suggesting their usage in non-time-critical scenarios to boost translation quality.
  arXiv  Detail & Related papers  (2023-02-28T00:50:19Z)
• idT5: Indonesian Version of Multilingual T5 Transformer [0.0]
  Indonesian is spoken by almost 200 million people and is the 10th most spoken language in the world. In this study, the mT5 model was adapted for only one language, Indonesian, resulting in a pre-trained T5 model that was specific only for Indonesian with a smaller size. Fine-tuned model based on our model achieved 77.18% accuracy on SA, 8% higher than the mT5-based model, and obtained nearly the same score as the mT5-based model on QG and QA.
  arXiv  Detail & Related papers  (2023-02-02T03:56:16Z)
• Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis [84.12658971655253]
  We propose Adapted Multimodal BERT, a BERT-based architecture for multimodal tasks. adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. In our ablations we see that this approach leads to efficient models, that can outperform their fine-tuned counterparts and are robust to input noise.
  arXiv  Detail & Related papers  (2022-12-01T17:31:42Z)
• Evaluation of Transfer Learning for Polish with a Text-to-Text Model [54.81823151748415]
  We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question answering. We present plT5 - a general-purpose text-to-text model for Polish that can be fine-tuned on various Natural Language Processing (NLP) tasks with a single training objective.
  arXiv  Detail & Related papers  (2022-05-18T09:17:14Z)
• Few-shot learning through contextual data augmentation [74.20290390065475]
  Machine translation models need to adapt to new data to maintain their performance over time. We show that adaptation on the scale of one to five examples is possible. Our model reports better accuracy scores than a reference system trained with on average 313 parallel examples.
  arXiv  Detail & Related papers  (2021-03-31T09:05:43Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• mT5: A massively multilingual pre-trained text-to-text transformer [60.0210636815514]
  "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on English-language NLP tasks. We introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages.
  arXiv  Detail & Related papers  (2020-10-22T17:58:14Z)

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.