Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns

• URL: http://arxiv.org/abs/2211.07628v1
• Date: Mon, 14 Nov 2022 18:50:16 GMT
• Title: Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns
• Authors: Shuyue Stella Li, Kenton Murray
• Abstract summary: We propose several Synthetic Code-Mixing (SCM) data augmentation methods that outperform the baseline on downstream sentiment analysis tasks. Our proposed methods demonstrate that strategically replacing parts of sentences in the matrix language with a constant mask significantly improves classification accuracy. We test our data augmentation method in a variety of low-resource and cross-lingual settings, reaching up to a relative improvement of 7.73% on the extremely scarce English-Malayalam dataset.
• Score: 0.5560631344057825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we focus on intrasentential code-mixing and propose several different Synthetic Code-Mixing (SCM) data augmentation methods that outperform the baseline on downstream sentiment analysis tasks across various amounts of labeled gold data. Most importantly, our proposed methods demonstrate that strategically replacing parts of sentences in the matrix language with a constant mask significantly improves classification accuracy, motivating further linguistic insights into the phenomenon of code-mixing. We test our data augmentation method in a variety of low-resource and cross-lingual settings, reaching up to a relative improvement of 7.73% on the extremely scarce English-Malayalam dataset. We conclude that the code-switch pattern in code-mixing sentences is also important for the model to learn. Finally, we propose a language-agnostic SCM algorithm that is cheap yet extremely helpful for low-resource languages.

Related papers

• Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis [0.0]
  Code-mixing (CM) is prevalent in multilingual societies but poses challenges for natural language processing. We propose using a large language model to generate synthetic CM data, which is then used to enhance the performance of task-specific models.
  arXiv  Detail & Related papers  (2024-11-01T15:52:09Z)
• MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization [81.83460411131931]
  In multilingual settings, non-Latin scripts and low-resource languages are usually disadvantaged in terms of language models' utility, efficiency, and cost. We propose multilingual adaptive gradient-based tokenization to reduce over-segmentation via adaptive gradient-based subword tokenization.
  arXiv  Detail & Related papers  (2024-07-11T18:59:21Z)
• Language Agnostic Code Embeddings [61.84835551549612]
  We focus on the cross-lingual capabilities of code embeddings across different programming languages. Code embeddings comprise two distinct components: one deeply tied to the nuances and syntax of a specific language, and the other remaining agnostic to these details. We show that when we isolate and eliminate this language-specific component, we witness significant improvements in downstream code retrieval tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:34:52Z)
• Leveraging Language Identification to Enhance Code-Mixed Text Classification [0.7340017786387767]
  Existing deep-learning models do not take advantage of the implicit language information in code-mixed text. Our study aims to improve BERT-based models performance on low-resource Code-Mixed Hindi-English datasets.
  arXiv  Detail & Related papers  (2023-06-08T06:43:10Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer [28.3684494647968]
  We propose a hierarchical transformer-based architecture (HIT) to learn the semantics of code-mixed languages. We evaluate the proposed method across 6 Indian languages and 9 NLP tasks on 17 datasets.
  arXiv  Detail & Related papers  (2022-04-27T07:50:18Z)
• Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching [41.88097793717185]
  Code-Switching (CS) is a common linguistic phenomenon in multilingual communities. This paper presents our investigations on end-to-end speech recognition for Mandarin-English CS speech.
  arXiv  Detail & Related papers  (2021-12-19T17:31:15Z)
• To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP [0.0]
  We investigate three categories of text augmentation methodologies which perform changes on the syntax. We compare them on part-of-speech tagging, dependency parsing and semantic role labeling for a diverse set of language families. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT.
  arXiv  Detail & Related papers  (2021-11-18T10:52:48Z)
• Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models [63.92643612630657]
  This paper attempts to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with language proximity. We derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks.
  arXiv  Detail & Related papers  (2020-10-12T17:26:34Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)
• Meta-Transfer Learning for Code-Switched Speech Recognition [72.84247387728999]
  We propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data.
  arXiv  Detail & Related papers  (2020-04-29T14:27:19Z)

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.