Cross-Lingual Training for Automatic Question Generation

By Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi. 2019

61 citations recorded by the source corpus.

Automatic question generation (QG) is a challenging problem in natural language understanding. QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer. For a new language, such training instances are hard to obtain making the QG problem even more challenging. Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g. English) to learn a QG model for a primary language (e.g. Hindi) of interest. For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset. We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages. We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese. Our proposed framework clearly outperforms a number of baseline models, including a fully-supervised transformer-based model trained on the QG datasets in the primary language. We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Question Answering & Reading Comprehension

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Citations in the corpus

MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, background): “Kumar et al. (2019) 1190 SQuAD instances from 240 paragraphs manually translated into 10 languages.”
Cross-Lingual Natural Language Generation via Pre-Training (2019, methodology): “Kumar et al. (2019) use training data annotated in multiple languages to jointly train a sequence-to-sequence model for question generation.”
QAmeleon: Multilingual QA with Only 5 Examples (2022, methodology): “Lee et al. (2018) propose to use automatically translated high-confidence QA examples for training, while other approaches (Kumar et al., 2019; Chi et al., 2020) only generate questions and require supervised training data in the target language.”
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2020, background): “Transfer learning techniques are often applied to these cross-lingual representations to overcome the dearth of non-English data (Cui et al., 2019a; Hsu et al., 2019; Lee and Lee, 2019; Kumar et al., 2019).”
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2020, background): “We note the exceptions of (Kumar et al., 2019) and (Chi et al.”
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text (2018, methodology): “In our work (Kumar et al., 2019a) we proposed a transformer based architecture to automatically generate complex multi-hop questions from knowledge graphs.”