GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval

• URL: http://arxiv.org/abs/2104.12741v1
• Date: Mon, 26 Apr 2021 17:34:31 GMT
• Title: GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval
• Authors: Timo M\"oller and Julian Risch and Malte Pietsch
• Abstract summary: We present GermanQuAD, a dataset of 13,722 extractive question/answer pairs. An extractive QA model trained on GermanQuAD significantly outperforms multilingual models.
• Score: 2.5621280373733604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major challenge of research on non-English machine reading for question answering (QA) is the lack of annotated datasets. In this paper, we present GermanQuAD, a dataset of 13,722 extractive question/answer pairs. To improve the reproducibility of the dataset creation approach and foster QA research on other languages, we summarize lessons learned and evaluate reformulation of question/answer pairs as a way to speed up the annotation process. An extractive QA model trained on GermanQuAD significantly outperforms multilingual models and also shows that machine-translated training data cannot fully substitute hand-annotated training data in the target language. Finally, we demonstrate the wide range of applications of GermanQuAD by adapting it to GermanDPR, a training dataset for dense passage retrieval (DPR), and train and evaluate the first non-English DPR model.

Related papers

• FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages [0.0]
  This paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. We employ fine-tuned, modest-scale models to establish benchmarks for both Question Generation (QG) and QA tasks within the translated datasets. We present a case study proposing a model for generating question-answer pairs, with an evaluation incorporating quality metrics such as question well-formedness, answerability, relevance, and children suitability.
  arXiv  Detail & Related papers  (2024-06-06T16:31:47Z)
• 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)
• Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model [75.66013048128302]
  In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training. We first identify the overoptimization problem during QE-based feedback training, manifested as an increase in reward while translation quality declines. To address the problem, we adopt a simple yet effective method that uses rules to detect the incorrect translations and assigns a penalty term to the reward scores of them.
  arXiv  Detail & Related papers  (2024-01-23T16:07:43Z)
• 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)
• PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale [53.92008514395125]
  PAXQA (Projecting annotations for cross-lingual (x) QA) decomposes cross-lingual QA into two stages. We propose a novel use of lexically-constrained machine translation, in which constrained entities are extracted from the parallel bitexts. We show that models fine-tuned on these datasets outperform prior synthetic data generation models over several extractive QA datasets.
  arXiv  Detail & Related papers  (2023-04-24T15:46:26Z)
• QAmeleon: Multilingual QA with Only 5 Examples [71.80611036543633]
  We show how to leverage pre-trained language models under a few-shot learning setting. Our approach, QAmeleon, uses a PLM to automatically generate multilingual data upon which QA models are trained. Prompt tuning the PLM for data synthesis with only five examples per language delivers accuracy superior to translation-based baselines.
  arXiv  Detail & Related papers  (2022-11-15T16:14:39Z)
• ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System [16.89747171947662]
  This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA) In this challenging scenario, given an input question the system has to gather evidence documents from a multilingual pool and generate an answer in the language of the question. We devised several approaches combining different model variants for three main components: Data Augmentation, Passage Retrieval, and Answer Generation.
  arXiv  Detail & Related papers  (2022-05-30T10:31:08Z)
• Towards More Equitable Question Answering Systems: How Much More Data Do You Need? [15.401330338654203]
  We take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. We make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems.
  arXiv  Detail & Related papers  (2021-05-28T21:32:04Z)
• Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering [8.558954185502012]
  We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data. We report a new state-of-the-art on four multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr)
  arXiv  Detail & Related papers  (2020-10-23T20:09:01Z)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

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.