What are the limits of cross-lingual dense passage retrieval for low-resource languages?

• URL: http://arxiv.org/abs/2408.11942v1
• Date: Wed, 21 Aug 2024 18:51:46 GMT
• Title: What are the limits of cross-lingual dense passage retrieval for low-resource languages?
• Authors: Jie Wu, Zhaochun Ren, Suzan Verberne,
• Abstract summary: We analyze the capabilities of the multi-lingual Passage Retriever (mDPR) for extremely low-resource languages. mDPR achieves success on multilingual open QA benchmarks across 26 languages, of which 9 were unseen during training. We focus on two extremely low-resource languages for which mDPR performs poorly: Amharic and Khmer.
• Score: 23.88853455670863
• License:
• Abstract: In this paper, we analyze the capabilities of the multi-lingual Dense Passage Retriever (mDPR) for extremely low-resource languages. In the Cross-lingual Open-Retrieval Answer Generation (CORA) pipeline, mDPR achieves success on multilingual open QA benchmarks across 26 languages, of which 9 were unseen during training. These results are promising for Question Answering (QA) for low-resource languages. We focus on two extremely low-resource languages for which mDPR performs poorly: Amharic and Khmer. We collect and curate datasets to train mDPR models using Translation Language Modeling (TLM) and question--passage alignment. We also investigate the effect of our extension on the language distribution in the retrieval results. Our results on the MKQA and AmQA datasets show that language alignment brings improvements to mDPR for the low-resource languages, but the improvements are modest and the results remain low. We conclude that fulfilling CORA's promise to enable multilingual open QA in extremely low-resource settings is challenging because the model, the data, and the evaluation approach are intertwined. Hence, all three need attention in follow-up work. We release our code for reproducibility and future work: https://anonymous.4open.science/r/Question-Answering-for-Low-Resource-Languages-B13C/

Related papers

• INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages [26.13077589552484]
  Indic-QA is the largest publicly available context-grounded question-answering dataset for 11 major Indian languages from two language families. We generate a synthetic dataset using the Gemini model to create question-answer pairs given a passage, which is then manually verified for quality assurance. We evaluate various multilingual Large Language Models and their instruction-fine-tuned variants on the benchmark and observe that their performance is subpar, particularly for low-resource languages.
  arXiv  Detail & Related papers  (2024-07-18T13:57:16Z)
• Can a Multichoice Dataset be Repurposed for Extractive Question Answering? [52.28197971066953]
  We repurposed the Belebele dataset (Bandarkar et al., 2023), which was designed for multiple-choice question answering (MCQA) We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic (MSA). Our aim is to enable others to adapt our approach for the 120+ other language variants in Belebele, many of which are deemed under-resourced.
  arXiv  Detail & Related papers  (2024-04-26T11:46:05Z)
• GlotLID: Language Identification for Low-Resource Languages [51.38634652914054]
  GlotLID-M is an LID model that satisfies the desiderata of wide coverage, reliability and efficiency. It identifies 1665 languages, a large increase in coverage compared to prior work.
  arXiv  Detail & Related papers  (2023-10-24T23:45:57Z)
• Evaluating and Modeling Attribution for Cross-Lingual Question Answering [80.4807682093432]
  This work is the first to study attribution for cross-lingual question answering. We collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. We find that a substantial portion of the answers is not attributable to any retrieved passages.
  arXiv  Detail & Related papers  (2023-05-23T17:57:46Z)
• MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages [4.433842217026879]
  Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. We augment the QA samples of the target language using translation and transliteration into other languages and use the augmented data to fine-tune an mBERT-based QA model. Experiments on the Google ChAII dataset show that fine-tuning the mBERT model with translations from the same language family boosts the question-answering performance.
  arXiv  Detail & Related papers  (2022-04-12T13:52:54Z)
• Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval [51.004601358498135]
  Mr. TyDi is a benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages. The goal of this resource is to spur research in dense retrieval techniques in non-English languages.
  arXiv  Detail & Related papers  (2021-08-19T16:53:43Z)
• One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval [39.061900747689094]
  CORA is a Cross-lingual Open-Retrieval Answer Generation model. It can answer questions across many languages even when language-specific annotated data or knowledge sources are unavailable.
  arXiv  Detail & Related papers  (2021-07-26T06:02:54Z)
• From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding [24.149299722716155]
  We introduce xSID, a new benchmark for cross-lingual Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. We propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification.
  arXiv  Detail & Related papers  (2021-05-15T23:51:11Z)
• Multilingual Answer Sentence Reranking via Automatically Translated Data [97.98885151955467]
  We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems. The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
  arXiv  Detail & Related papers  (2021-02-20T03:52:08Z)
• Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval [4.4973334555746]
  Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English. We formulate a task setup more realistic to available resources, that circumvents document retrieval to reliably transfer knowledge from English to lower resource languages. Within this task setup we propose Reranked Maximal Inner Product Search (RM-MIPS), akin to semantic similarity retrieval over the English training set with reranking.
  arXiv  Detail & Related papers  (2020-12-28T04:38:45Z)
• 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)

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.