Stay Together: A System for Single and Split-antecedent Anaphora Resolution (2021)

Abstract: The state-of-the-art on basic, single-antecedent anaphora has greatly improved in recent years. Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in “Time-Warner is considering a legal challenge to Telecommunications Inc’s plan to buy half of Showtime Networks Inc–a move that could lead to all-out war between the two powerful companies”. Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available. These systems also focus on split-antecedent anaphors only. In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics.

## introduction thanks in part to the latest developments in deep neural network architectures and contextual word embeddings (e.g., elmo @xcite and bert @xcite ), the performance of models for single-antecedent anaphora resolution has greatly improved @xcite @xcite @xcite . so recently, the attention has turned to more complex cases of anaphora, such as anaphora requiring some sort of commonsense knowledge as in the winograd schema challenge @xcite @xcite ; pronominal anaphors that cannot be resolved purely using gender @xcite , bridging reference @xcite , discourse deixis @xcite @xcite and, finally, split-antecedent anaphora @xcite ) -plural anaphoric reference in which the two antecedents are not part of a single noun phrase. however, a number of hurdles have to be tackled when trying to study these cases of anaphora, ranging from the lack of annotated resources to the rarity of some of these phenomena in the existing ones. thus, most previous work on resolving these anaphoric relations focused on developing dedicated systems for the specific task. the systems are usually enhanced by transfer-learning to utilise extra resources, as those anaphoric relations are sparsely annotated. the most frequently used extra resource is single-antecedent anaphors. due to the complexity of these tasks, previous work is usually based on assuming that either gold anaphors @xcite or gold mentions @xcite are provided. by contrast, in this work we introduce a system that resolves both single and split-antecedent anaphors, and is evaluated in a more realistic setting that does not rely on gold anaphors/mentions. we evaluate our system on the arrau corpus @xcite , in which both single and split-antecedent anaphors are annotated, although the latter are much rarer than the former. we use the state-of-the-art coreference resolution system on arrau @xcite ) as our base system for single-antecedent anaphors. this cluster-ranking system interprets single-antecedent anaphors, singletons and non-referring expressions jointly. in this work, we extend the system to resolve split-antecedent anaphors. the extended part of the system shares mention representations and candidate clusters with the base system, and outputs binary decisions between a mention and individual candidate clusters. we configure our system to learn the split-antecedent part and the base system in both joint and pre-trained fashion. the results show both versions work much better than naive baselines based on heuristics and random selection. the pre-trained version works equally well as the joint version on splitantecedent anaphors, but it is better for the other aspects of anaphoric interpretation. in the paper we also begin to address the question of how a system carrying out both single and splitantecedent anaphora resolution should be evaluated. specifically, we introduce an extended version of lea @xcite , a standard coreference metric which can be used to give partial credit for resolution, to evaluate single and splitantecedent anaphors together. using this metric, we find that our best model achieves a better lea score than the baselines. we further evaluate our best system in the gold setting to compare with the @xcite system. the model achieved better performance when compared to their system that is designed solely for split-antecedent task. ## evaluating coreference chains with split antecedents if the interpretation of a split-antecedent anaphor were only given credit when all antecedents are correctly detected and grouped together, without giving any reward to systems that find at least some of the antecedents, systems that get closer to the gold would be unfairly penalized, particularly for the cases with 3 or more split antecedents (25% in our data). consider example 4.1, in which "their i,j " refers to the set {"mary", "john"}, and "they i,j,p " to the set {"mary", "john", "jane"}. and take two systems a and b that resolve "their i,j " to {"alex", "jane"} and {"mary", "jane"}, respectively and "they i,j,p " to {"alex"} and {"mary i ", "john j "}, respectively. neither system is perfect, but intuitively, system b is more accurate in resolving split-antecedent anaphors (it correctly identifies 1 antecedent of "their i,j " and 2 of "they i,j,p ", versus 0 for a)-yet both systems will receive the same 0 score if only a perfect match is credited. example 4.1. mary i and john j were on their way to visit alex k when mary i saw jane p on their i,j way and realized they i,j,p all wore the same shirt. this example indicates that in order to score a system carrying out both single and split-antecedent resolution three issues have to be addressed. first of all, it is necessary to have some way to represent plural entities. second, we need some way of ensuring that systems that propose different but equivalent resolutions for splitantecedent plurals score the same. third, we need a metric allowing some form of partial credit. 2 we discuss how we addressed each issue in turn. plural mentions first of all, we propose to have two types of mentions in our coreference chains: in addition to the standard individual mentions ("mary"), we also allow plural mentions ({"mary", "jane"}). normalizing coreference chains as example 4.1 shows, a text may contain multiple individual mentions of the same entity that participate in a plural mention (e.g. 'mary'). plural mentions whose antecedents are mentions of the same entity should be equivalent. to do this, we use the first mention of each gold coreference chains as the representative of the entity. we normalize every plural mention in a system-produced coreference chain by (i) aligning the system-produced coreference chains for the individual mentions in the plural mention to the gold coreference chains using ceaf, and (ii) replacing each individual mention in the plural mention with the first mention in the aligned gold coreference chains. partial credit a natural way to obtain a scorer for coreference resolution giving partial credit is to extend the lea evaluation metric @xcite let e be an entity in the system output e consisting of n mentions, and k be the set of gold entities. the resolution-score (rs) of e is computed as: to extend lea to handle split-antecedents, we change b to also reward a system if any of the corresponding mentions of l, i.e., mentions that are connected by l, is a plural mention and is partially resolved. let p(m) be an ordered list of all subsets of m, including m, by descending order of their size. if m is a singular mention, p will only contain {m}. if m is a plural mention, p will contain m as well as all the subsets of m's containing mentions. for instance, p({"mary", "john"})=[ {"mary", "john"}, {"john"}, {"mary"}]. assuming the corresponding mentions of l are m i and m j , we update b(l, k) as follows: for instance, consider the system chain {m 1 ={"mary", "jane"}, m 2 ="their i,j "} for example 4.1. the coreference link between m 1 and m 2 does not exist in the gold entities. however, m 1 is a subset of a gold mention, i.e., m @xmath0, "john", "jane"}, and m 1 ⊂ p(m k ). therefore, system b receives a reward of 2 * 1 3 * 1 for resolving the coreference link between m 1 and m 2 based on rs. importance as discussed, the number of entities that contain split-antecedents in our annotated data is negligible compared to entities with singular mentions. therefore, we will not see a big difference in the overall score when the system resolves both singular and plural mentions. in order to put more emphasize on harder coreference links, i.e., resolving split-antecedents, we adapt the importance measure to assign a higher weight to entities containing split-antecedent as follows: for joint evaluation of single/split-antecedent anaphors, we report the lea score using the upgraded script described in section 4. ## conclusions in this paper, we introduced a neural system performing both single and split-antecedent anaphora in addition, we also proposed an extension of the lea coreference evaluation metric to evaluate both single and split-antecedent anaphors in a single metric.