Cross-Lingual Zero Pronoun Resolution

In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certainsyntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifyingand resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavilyonsemanticcoherenceandlexicalrelationships. WeproposeaBERT-basedcross-lingualmodelforzeropronounresolution,andevaluateit on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolutionfor Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction andfine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating whichlayer encodes the most suitable representation for the task. Our code is available at https://github.com/amaloraini/cross-lingual-Z

A Cluster Ranking Model for Full Anaphora Resolution

Anaphora resolution (coreference) systems designed for theCONLL2012 dataset typically cannot handle key aspects of the full anaphoraresolution task such as the identification of singletons and of certain types of non-referring expressions (e.g., expletives), as these aspectsare not annotated in that corpus. However, the recently releasedCRAC2018 Shared Task and Phrase Detectives (PD) datasets can nowbe used for that purpose. In this paper, we introduce an architecture to simultaneously identify non-referring expressions (includingexpletives, predicativeNPs, and other types) and build coreference chains, including singletons. Our cluster-ranking system uses anattention mechanism to determine the relative importance of the mentions in the same cluster. Additional classifiers are used to identifysingletons and non-referring markables. Our contributions are as follows. First of all, we report the first result on theCRACdata usingsystem mentions; our result is 5.8% better than the shared task baseline system, which used gold mentions. Our system also outperformsthe best-reported system onPDby up to 5.3%. Second, we demonstrate that the availability of singleton clusters and non-referringexpressions can lead to substantially improved performance on non-singleton clusters as well. Third, we show that despite our model notbeing designed specifically for theCONLLdata, it achieves a very competitive result

Neural Mention Detection

Mention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle the full range of mentions. In this work, we propose and compare three neural network-based approaches to mention detection. The first approach is based on the mention detection part of a state of the art coreference resolution system; the second uses ELMO embeddings together with a bidirectional LSTM and a biaffine classifier; the third approach uses the recently introduced BERT model. Our best model (using a biaffine classifier) achieves gains of up to 1.8 percentage points on mention recall when compared with a strong baseline in a HIGH RECALL coreference annotation setting. The same model achieves improvements of up to 5.3 and 6.2 p.p. when compared with the best-reported mention detection F1 on the CONLL and CRAC coreference data sets respectively in a HIGH F1 annotation setting. We then evaluate our models for coreference resolution by using mentions predicted by our best model in start-of-the-art coreference systems. The enhanced model achieved absolute improvements of up to 1.7 and 0.7 p.p. when compared with our strong baseline systems (pipeline system and end-to-end system) respectively. For nested NER, the evaluation of our model on the GENIA corpora shows that our model matches or outperforms state-of-the-art models despite not being specifically designed for this task