On BabyLM 10M/100M data, teacher-less mutual learning with learned student weights improves over RoBERTa-base by 1-3% on BLiMP, yet simple self-distillation outperforms the proposed DWML.
Open Subtitles Paraphrase Corpus for Six Languages
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abstract
This paper accompanies the release of Opusparcus, a new paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The corpus consists of paraphrases, that is, pairs of sentences in the same language that mean approximately the same thing. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows. The informal and colloquial genre that occurs in subtitles makes such data a very interesting language resource, for instance, from the perspective of computer assisted language learning. For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been checked manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators.
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When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?
On BabyLM 10M/100M data, teacher-less mutual learning with learned student weights improves over RoBERTa-base by 1-3% on BLiMP, yet simple self-distillation outperforms the proposed DWML.