Cross-lingual Emotion Detection

• URL: http://arxiv.org/abs/2106.06017v1
• Date: Thu, 10 Jun 2021 19:52:06 GMT
• Title: Cross-lingual Emotion Detection
• Authors: Sabit Hassan, Shaden Shaar, Kareem Darwish
• Abstract summary: We consider English as the source language with Arabic and Spanish as target languages. Our BERT-based monolingual models that are trained on target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute Jaccard score for Arabic and Spanish respectively. Next, we show that using cross-lingual approaches with English data alone, we can achieve more than 90% and 80% relative effectiveness of the Arabic and Spanish BERT models respectively.
• Score: 6.767035411834297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion detection is of great importance for understanding humans. Constructing annotated datasets to train automated models can be expensive. We explore the efficacy of cross-lingual approaches that would use data from a source language to build models for emotion detection in a target language. We compare three approaches, namely: i) using inherently multilingual models; ii) translating training data into the target language; and iii) using an automatically tagged parallel corpus. In our study, we consider English as the source language with Arabic and Spanish as target languages. We study the effectiveness of different classification models such as BERT and SVMs trained with different features. Our BERT-based monolingual models that are trained on target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute Jaccard score for Arabic and Spanish respectively. Next, we show that using cross-lingual approaches with English data alone, we can achieve more than 90% and 80% relative effectiveness of the Arabic and Spanish BERT models respectively. Lastly, we use LIME to interpret the differences between models.

Related papers

• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning [14.02101305717738]
  Multilingual large language models (MLLMs) are jointly trained on data from many different languages. It remains unclear to what extent, and under which conditions, languages rely on each other's data. We find that MLLMs rely on data from multiple languages from the early stages of fine-tuning and that this reliance gradually increases as fine-tuning progresses.
  arXiv  Detail & Related papers  (2023-05-22T17:47:41Z)
• Learning Cross-lingual Visual Speech Representations [108.68531445641769]
  Cross-lingual self-supervised visual representation learning has been a growing research topic in the last few years. We use the recently-proposed Raw Audio-Visual Speechs (RAVEn) framework to pre-train an audio-visual model with unlabelled data. Our experiments show that: (1) multi-lingual models with more data outperform monolingual ones, but, when keeping the amount of data fixed, monolingual models tend to reach better performance.
  arXiv  Detail & Related papers  (2023-03-14T17:05:08Z)
• Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer [4.554080966463776]
  Multi-lingual language models (LM) have been remarkably successful in enabling natural language tasks in low-resource languages. We try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-12-04T07:22:21Z)
• cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation [6.381149074212897]
  We propose a pipeline that utilizes English-only vision-language models to train a monolingual model for a target language. We release a large-scale visual question answering dataset in Japanese and Hindi language. Our pipeline outperforms the current state-of-the-art models by a relative increase of 4.4% and 13.4% respectively in accuracy.
  arXiv  Detail & Related papers  (2022-06-07T14:46:30Z)
• A Multilingual Modeling Method for Span-Extraction Reading Comprehension [2.4905424368103444]
  We propose a multilingual extractive reading comprehension approach called XLRC. We show that our model outperforms the state-of-the-art baseline (i.e., RoBERTa_Large) on the CMRC 2018 task.
  arXiv  Detail & Related papers  (2021-05-31T11:05:30Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [52.69730591919885]
  We present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. We observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
  arXiv  Detail & Related papers  (2020-07-29T19:38:35Z)
• 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)
• A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT [60.9051207862378]
  multilingual BERT works remarkably well on cross-lingual transfer tasks. Datasize and context window size are crucial factors to the transferability. There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
  arXiv  Detail & Related papers  (2020-04-20T11:13:16Z)

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.