Existing multiparty dialogue datasets for 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 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.

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/2af6a9c0-c16a-48eb-a3d6-4fd3951826a9" width="300" height="400"></iframe>

Multilingual Coreference Resolution in Multiparty Dialogue

We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.

An Empirical Study on Finding Spans

Discourse and Pragmatics

technical paper

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Multilingual Coreference Resolution in Multiparty Dialogue

An Empirical Study on Finding Spans

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