Triad-based Neural Network for Coreference Resolution

Proceedings of the 27th International Conference on Computational Linguistics, pages 35–43 Santa Fe, New Mexico, USA, August 20-26, 2018. 35 Triad-based Neural Network for Coreference Resolution Yuanliang Meng Text Machine Lab for NLP Department of Computer Science University of Massachusetts Lowell ymeng@cs.uml.edu Anna Rumshisky Text Machine Lab for NLP Department of Computer Science University of Massachusetts Lowell arum@cs.uml.edu Abstract We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level. 1 Introduction Entity coreference resolution aims to identify mentions that refer to the same entity. A mention is a piece of text, usually a noun, a pronoun, or a nominal phrase. Resolving coreference often requires understanding the full context, and sometimes also world knowledge not provided in the text. Generally speaking, three types of models have been used for coreference resolution: pairwise models, mention ranking models, and entity-mention models. The first two are more common in literature, and the third one is somewhat less studied. Pairwise models a.k.a. mention pair models build a binary classifier over pairs of mentions (Soon et al., 2001; McCallum and Wellner, 2003). If all the pairs are classified correctly, then all coreferent mentions are identified. The mention ranking models do not rely on the full pairwise classification, but rather compare each mention to its possible antecedents in order to determine whether the mention might refer to an existing antecedent or starts a new coreference chain (Durrett and Klein, 2013; Wiseman et al., 2016; Clark and Manning, 2016). The entity-mention models try constructing representations of discourse entities, and associating different mentions with the entity representations (Luo et al., 2004). However, none of these model types consider more than two mentions together at the low level. By low level here, we mean the processing of input mention features, as opposed to processing of constructed representations. Pairwise models and mention ranking models make low-level decisions on mention pairs only. Some further processing may be applied to reconcile global scope conflicts, but this process no longer relies directly on mention features. This paper proposes a neural network model which works on triads of mentions directly. Each time, the system takes three mentions as input, and decisions on their coreference relations are made while taking into account all mutual …

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