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Generating dictionary definitions automatically can prove useful for language learners. However, its still a challenging task of cross-lingual definition generation. In this work, we propose to generate definitions in English for words in various languages. To achieve this, we present a simple yet effective approach based on publicly available pretrained language models. In this approach, models can be directly applied to other languages after trained on the English dataset. We demonstrate the effectiveness of this approach on zero-shot definition generation. Experiments and manual analyses on newly constructed datasets show that our models have a strong cross-lingual transfer ability and can generate fluent English definitions for Chinese words. We further measure the lexical complexity of generated and reference definitions. The results show that the generated definitions are much simpler, which is more suitable for language learners.
Despite the recent advancement in NLP research, cross-lingual transfer for natural language generation is relatively understudied. In this work, we transfer supervision from high resource language (HRL) to multiple low-resource languages (LRLs) for n
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two
Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, according to previous studies on vision-language navigation (VLN). To go bey
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual
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