El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks

• URL: http://arxiv.org/abs/2007.01955v1
• Date: Fri, 3 Jul 2020 22:22:44 GMT
• Title: El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
• Authors: Maria Khvalchik and Mikhail Galkin
• Abstract summary: Pre-training large-scale language models (LMs) requires huge amounts of text corpora. We study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks. We show that careful curation along with post-processing lead to improved performance and overall LMs robustness.
• Score: 0.12183405753834563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-training large-scale language models (LMs) requires huge amounts of text corpora. LMs for English enjoy ever growing corpora of diverse language resources. However, less resourced languages and their mono- and multilingual LMs often struggle to obtain bigger datasets. A typical approach in this case implies using machine translation of English corpora to a target language. In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness. In the empirical evaluation, we perform a comparison of directly translated against curated Spanish SQuAD datasets on both user and system levels. Further experimental results on XQuAD and MLQA transfer-learning evaluation question answering tasks show that presumably multilingual LMs exhibit more resilience to machine translation artifacts in terms of the exact match score.

Related papers

• What do Large Language Models Need for Machine Translation Evaluation? [12.42394213466485]
  Large language models (LLMs) can achieve results comparable to fine-tuned multilingual pre-trained language models. This paper explores what translation information, such as the source, reference, translation errors and annotation guidelines, is needed for LLMs to evaluate machine translation quality.
  arXiv  Detail & Related papers  (2024-10-04T09:50:45Z)
• Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem [4.830018386227]
  This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resource language as part of an automated machine translation pipeline. We conduct a set of experiments translating Southern Quechua to Spanish and examine the informativity of various types of context retrieved from a constrained database of digitized pedagogical materials and parallel corpora.
  arXiv  Detail & Related papers  (2024-06-21T20:02:22Z)
• On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations? [19.346078451375693]
  We present an analysis of existing evaluation frameworks in NLP. We propose several directions for more robust and reliable evaluation practices. We show that simpler baselines can achieve relatively strong performance without having benefited from large-scale multilingual pretraining.
  arXiv  Detail & Related papers  (2024-06-20T12:46:12Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation [64.5862977630713]
  This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task. We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive.
  arXiv  Detail & Related papers  (2024-01-12T13:23:21Z)
• Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages [51.301942056881146]
  We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages. Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba) We examine cross-lingual reranking with queries in English and passages in the African languages.
  arXiv  Detail & Related papers  (2023-12-26T18:38:54Z)
• Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
  Existing large language models show disparate capability across different languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
  arXiv  Detail & Related papers  (2023-08-09T13:32:06Z)
• Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions [68.01449013641532]
  Large-scale Pretrained Language Models (LLMs) have shown strong abilities in multilingual translations. We present a detailed analysis by finetuning a multilingual pretrained language model, XGLM-7B, to perform multilingual translation.
  arXiv  Detail & Related papers  (2023-05-24T12:00:24Z)
• 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)
• Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT [129.99918589405675]
  We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
  arXiv  Detail & Related papers  (2020-09-16T11:37:10Z)

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.