Multilingual Coreference Resolution in Multiparty Dialogue
This addresses the problem of limited data for researchers in NLP working on coreference resolution in multiparty and multilingual contexts, though it is incremental as it builds on existing annotation projection methods.
The authors tackled the lack of large-scale datasets for multilingual coreference resolution in multiparty dialogue by creating MMC, a dataset based on TV transcripts, and found that off-the-shelf models perform poorly on it, while silver data from annotation projection works well for augmentation and zero-shot cross-lingual training.
Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference resolution data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.