The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including nonreferring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Taskidentity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.

Common technologies for automatic coreference resolution require either a language-specific rule set or large collections of manually annotated data, which is typically limited to newswire texts in major languages. This makes it difficult to develop coreference resolvers for a large number of the so-called low-resourced languages. We apply a direct projection algorithm on a multi-genre and multilingual corpus (English, German, Russian) to automatically produce coreference annotations for two target languages without exploiting any linguistic knowledge of the languages. Our evaluation of the projected annotations shows promising results, and the error analysis reveals structural differences of referring expressions and coreference chains for the three languages, which can now be targeted with more linguistically-informed projection algorithms.

In this paper, we examine the possibility of using annotation projection from multiple sources for automatically obtaining coreference annotations in the target language. We implement a multi-source annotation projection algorithm and apply it on an English-German-Russian parallel corpus in order to transfer coreference chains from two sources to the target side. Operating in two settings -a low-resource and a more linguistically-informed onewe show that automatic coreference transfer could benefit from combining information from multiple languages, and assess the quality of both the extraction and the linking of target coreference mentions.

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We report on experiments to validate and extend two language-specific connective databases (German and Italian) using a word-aligned corpus. This is a first step toward constructing a bilingual lexicon on connectives that are connected via their discourse senses.