Babel detects and repairs stylistic mismatches in machine translation outputs using a style detector and a diffusion-based applicator trained on monolingual corpora.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
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abstract
Named Entity Recognition and Relation Extraction for Chinese literature text is regarded as the highly difficult problem, partially because of the lack of tagging sets. In this paper, we build a discourse-level dataset from hundreds of Chinese literature articles for improving this task. To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method. Based on this corpus, we also introduce several widely used models to conduct experiments. Experimental results not only show the usefulness of the proposed dataset, but also provide baselines for further research. The dataset is available at https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
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Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only
Babel detects and repairs stylistic mismatches in machine translation outputs using a style detector and a diffusion-based applicator trained on monolingual corpora.