Cross-Lingual Transfer Learning for Complex Word Identification

• URL: http://arxiv.org/abs/2010.01108v1
• Date: Fri, 2 Oct 2020 17:09:47 GMT
• Title: Cross-Lingual Transfer Learning for Complex Word Identification
• Authors: George-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu
• Abstract summary: Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words in texts. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks. Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment.
• Score: 0.3437656066916039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words, or groups of words, in texts from different areas of expertise. The purpose of CWI is to highlight problematic structures that non-native speakers would usually find difficult to understand. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e., Transformers). Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment by relying on the CWI shared task 2018 dataset available for four different languages (i.e., English, German, Spanish, and also French). Our approach surpasses state-of-the-art cross-lingual results in terms of macro F1-score on English (0.774), German (0.782), and Spanish (0.734) languages, for the zero-shot learning scenario. At the same time, our model also outperforms the state-of-the-art monolingual result for German (0.795 macro F1-score).

Related papers

• 1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of Language, Hate Speech, and Targets using LLMs [0.0]
  This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task. We focus on language detection, hate speech identification, and target detection in Devanagari script languages.
  arXiv  Detail & Related papers  (2024-11-11T10:34:36Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Meta-Learning a Cross-lingual Manifold for Semantic Parsing [75.26271012018861]
  Localizing a semantic to support new languages requires effective cross-lingual generalization. We introduce a first-order meta-learning algorithm to train a semantic annotated with maximal sample efficiency during cross-lingual transfer. Results across six languages on ATIS demonstrate that our combination of steps yields accurate semantics sampling $le$10% of source training data in each new language.
  arXiv  Detail & Related papers  (2022-09-26T10:42:17Z)
• GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding [74.39024160277809]
  We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance. GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.
  arXiv  Detail & Related papers  (2022-04-18T13:56:58Z)
• Meta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation [11.155430893354769]
  This paper proposes a novel meta-learning framework to learn shareable structures from typologically diverse languages. We first cluster the languages based on language representations and identify the centroid language of each cluster. A meta-learning algorithm is trained with all centroid languages and evaluated on the other languages in the zero-shot setting.
  arXiv  Detail & Related papers  (2022-03-19T05:22:07Z)
• Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure [54.01613740115601]
  We study three language properties: constituent order, composition and word co-occurrence. Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
  arXiv  Detail & Related papers  (2022-03-16T07:09:35Z)
• 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)
• On Learning Universal Representations Across Languages [37.555675157198145]
  We extend existing approaches to learn sentence-level representations and show the effectiveness on cross-lingual understanding and generation. Specifically, we propose a Hierarchical Contrastive Learning (HiCTL) method to learn universal representations for parallel sentences distributed in one or multiple languages. We conduct evaluations on two challenging cross-lingual tasks, XTREME and machine translation.
  arXiv  Detail & Related papers  (2020-07-31T10:58:39Z)
• That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages [72.9927937955371]
  We use the resources existing in other languages to train a multilingual automatic speech recognition model. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages.
  arXiv  Detail & Related papers  (2020-05-16T22:28:09Z)
• Zero-Shot Cross-Lingual Transfer with Meta Learning [45.29398184889296]
  We consider the setting of training models on multiple languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks.
  arXiv  Detail & Related papers  (2020-03-05T16:07:32Z)

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.