Detecting Text Formality: A Study of Text Classification Approaches

• URL: http://arxiv.org/abs/2204.08975v2
• Date: Fri, 8 Sep 2023 09:11:02 GMT
• Title: Detecting Text Formality: A Study of Text Classification Approaches
• Authors: Daryna Dementieva, Nikolay Babakov and Alexander Panchenko
• Abstract summary: This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods. We conducted three types of experiments -- monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task.
• Score: 78.11745751651708
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation -- GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments -- monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.

Related papers

• Machine Translation to Control Formality Features in the Target Language [0.9208007322096532]
  This research explores how machine learning methods are used to translate from English to languages with formality. It was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model. We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth.
  arXiv  Detail & Related papers  (2023-11-22T15:42:51Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification [50.675552118811]
  Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
  arXiv  Detail & Related papers  (2023-06-08T07:33:22Z)
• Towards Best Practices for Training Multilingual Dense Retrieval Models [54.91016739123398]
  We focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Our study is organized as a "best practices" guide for training multilingual dense retrieval models.
  arXiv  Detail & Related papers  (2022-04-05T17:12:53Z)
• GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation [9.501648136713694]
  Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. This paper proposes a novel data augmentation technique that leverages large-scale language models to generate realistic text samples.
  arXiv  Detail & Related papers  (2021-04-18T11:39:33Z)
• Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation [127.81351683335143]
  Cross-lingual pretraining requires models to align the lexical- and high-level representations of the two languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings.
  arXiv  Detail & Related papers  (2021-03-18T21:17:58Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks [1.2691047660244335]
  BERT (Bidirectional Representations from Transformers) and ALBERT (A Lite BERT) are methods for pre-training language models. We make available our trained BERT and Albert model for Portuguese.
  arXiv  Detail & Related papers  (2020-07-19T19:13:20Z)

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.