Machine Translation to Control Formality Features in the Target Language

• URL: http://arxiv.org/abs/2311.13475v1
• Date: Wed, 22 Nov 2023 15:42:51 GMT
• Title: Machine Translation to Control Formality Features in the Target Language
• Authors: Harshita Tyagi, Prashasta Jung, Hyowon Lee
• Abstract summary: 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.
• Score: 0.9208007322096532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Formality plays a significant role in language communication, especially in low-resource languages such as Hindi, Japanese and Korean. These languages utilise formal and informal expressions to convey messages based on social contexts and relationships. When a language translation technique is used to translate from a source language that does not pertain the formality (e.g. English) to a target language that does, there is a missing information on formality that could be a challenge in producing an accurate outcome. This research explores how this issue should be resolved when machine learning methods are used to translate from English to languages with formality, using Hindi as the example data. This was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model in a similar setting. Since there are not a lot of training data with ground truth, automated annotation techniques were employed to increase the data size. The primary modeling approach involved leveraging transformer models, which have demonstrated effectiveness in various natural language processing tasks. We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth. This metric provides a quantitative measure of how well the translations align with the desired outputs. Our study showcases a versatile translation strategy that considers the nuances of formality in the target language, catering to diverse language communication needs and scenarios.

Related papers

• 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)
• In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages [2.457872341625575]
  In this work, we focus on one language property highly influenced by culture: formality. We analyze the formality distributions of XGLM and BLOOM's predictions, two popular generative multilingual language models, in 5 languages. We classify 1,200 generations per language as formal, informal, or incohesive and measure the impact of the prompt formality on the predictions.
  arXiv  Detail & Related papers  (2023-02-23T19:39:52Z)
• Controlling Translation Formality Using Pre-trained Multilingual Language Models [19.465727478912072]
  This paper describes the University of Maryland's submission to the Special Task on Formality Control for Spoken Language Translation at iwslt. We investigate to what extent this problem can be addressed with a textitsingle multilingual model. Results show that this strategy can approach the translation quality and formality control achieved by dedicated translation models.
  arXiv  Detail & Related papers  (2022-05-13T13:47:28Z)
• Detecting Text Formality: A Study of Text Classification Approaches [78.11745751651708]
  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.
  arXiv  Detail & Related papers  (2022-04-19T16:23:07Z)
• Language Contamination Explains the Cross-lingual Capabilities of English Pretrained Models [79.38278330678965]
  We find that common English pretraining corpora contain significant amounts of non-English text. This leads to hundreds of millions of foreign language tokens in large-scale datasets. We then demonstrate that even these small percentages of non-English data facilitate cross-lingual transfer for models trained on them.
  arXiv  Detail & Related papers  (2022-04-17T23:56:54Z)
• Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data [2.225882303328135]
  We propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parsing task. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems.
  arXiv  Detail & Related papers  (2021-09-09T14:51:11Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards [40.17497211507507]
  Cross-lingual text summarization is a practically important but under-explored task. We propose an end-to-end cross-lingual text summarization model.
  arXiv  Detail & Related papers  (2020-06-27T21:51:38Z)
• Data Annealing for Informal Language Understanding Tasks [66.2988222278475]
  We propose a data annealing transfer learning procedure to bridge the performance gap on informal language tasks. It successfully utilizes a pre-trained model such as BERT in informal language.
  arXiv  Detail & Related papers  (2020-04-24T09:27:09Z)

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.