Bridging Resolution: Making Sense of the State of the Art (2021)

Abstract: While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.

## introduction bridging resolution is an anaphora resolution task that involves identifying and resolving bridging/associative anaphors, which are anaphoric references to non-identical associated antecedents. to exemplify, consider the following sentences taken from the bashi corpus @xcite : even if baseball triggers losses at cbsand he doesn't think it will -"i'd rather see the games on our air than on nbc and abc," he says . in this example, a bridging link exists between the anaphor the games and its antecedent baseball, as the definite description cannot be interpreted correctly unless it is associated with baseball. bridging resolution is arguably more challenging than entity coreference resolution. the reason is that unlike in entity coreference, in bridging resolution there are typically no clear syntactic or surface clues for identifying the antecedent of a bridging anaphor. in many cases, resolution requires the use of context as well as commonsense inference. despite the difficulty of bridging resolution, the annotated corpora available for training bridging resolvers are much smaller than those for training entity coreference resolvers (e.g., ontonotes @xcite ). as a result, early work has focused on developing rule-based systems (e.g., @xcite al. (2014), rösiger (2018b) ). a key weakness of rule-based approaches is that the ruleset may have to be updated when it is applied to a new corpus (e.g., new rules may have to be added, and existing rules may have to be removed or modified), as different bridging corpora are annotated with slightly different guidelines (to cover different kinds of bridging links, for instance). in light of this weakness, @xcite have recently proposed a neural bridging resolver based on multi-task learning (mtl). despite being trained on the relatively small amount of labeled data that are currently available, their resolver has achieved state-of-the-art results on three evaluation corpora. in this paper, we seek to make sense of this state of the art by shedding light on two issues. first, how is the mtl model better than its rule-based counterparts? more specifically, while mtl is apparently making fewer mistakes than the rules, are the two approaches making similar or complementary mistakes? second, given that the mtl model is the current state of the art, what needs to be improved in mtl? to investigate the first issue, we propose a hybrid approach to bridging resolution: we first apply the hand-crafted rules to identify bridging links, and then employ the mtl-based model to resolve any (anaphoric) mentions that are not resolved by the rules. the design of this pipelined resolver is motivated in part by sieve-based approaches to entity coreference resolution @xcite . specifically, given our hypothesis that hand-crafted rules typically have higher precision and lower coverage than machinelearned patterns, we employ the rules as our first sieve and mtl as our second sieve. if our hybrid approach outperformed both the rule-based and learning-based approaches, that would provide suggestive evidence that these two approaches have different strengths and weaknesses and therefore should be viewed as complementary approaches to bridging resolution. note that this would be an important ramification, as learning-based approaches and rule-based approaches to bridging resolution have thus far been viewed as competing approaches. for instance, when evaluating their mtl model, @xcite merely view the rule-based systems as baselines. to investigate the second issue, we perform a manual analysis of the major types of error made by mtl. since interpretability remains a key weaknesses of neural models, we believe that our analysis could provide useful insights into what needs to be improved in mtl. for arrau rst, we use the standard traintest split. for isnotes and bashi, we divide the available documents into 10 folds and report 10fold cross validation results, following previous work @xcite . the hybrid approach. recall that our hybrid approach is composed of a rule-based system and @xcite (learning-based) mtl approach. below we provide a brief overview of the mtl approach and the rules. yu and poesio's (2020) mtl-based system is the first neural model for full bridging resolution. 1 they presented two extensions to @xcite span-based neural mentionranking model @xcite that was originally developed for entity coreference resolution. first, they provided gold mentions as input to the model, meaning that the model needs to learn the span representations but not the span boundaries. second, they proposed to train the model to perform coreference and bridging in a mtl framework, where the span representation layer is shared by the two tasks so that information learned from one task can be utilized when learning the other task. unlike feature-based approaches, where feature engineering plays a critical role in performance, this model employs only two features, the length of a mention and mention-pair distance. different rule-based systems have been developed for the three evaluation corpora. we used @xcite rules for isnotes, and @xcite rulesets for bashi and arrau. foot_1 postprocessing. following previous work @xcite , we postprocess the output of a resolver by removing the gold coreferent anaphors from the predicted bridging anaphors. evaluation metrics. we report results for recognition and resolution in terms of precision, recall, and f-score. for recognition, recall is the fraction of gold anaphors that are correctly identified, whereas precision is the fraction of anaphors iden- ## results bridging recognition and resolution results of the three approaches under comparison (i.e., rules, mtl, and hybrid) on the three evaluation corpora are shown in next, we compare in a few points about the results in to understand the reason, we took a closer look at the documents in bashi and found that there are cases of bridging that are not being annotated. examples of such missing bridging links are shown in in ## error analysis to better understand what areas of improvement are needed by the mtl model, we perform a manual analysis of its errors and discuss three major types of error in the following three subsections. ## conclusion in this paper, we sought to make sense of the state of the art in bridging resolution. we combined the hand-crafted rules and the mtl model in a pipelined fashion, showing that (1) the rules and mtl were making complementary mistakes and (2) the resulting hybrid approach achieved state-ofthe-art results on three standard evaluation datasets. in addition, we performed a manual error analysis to determine what needed to be improved in mtl. finally, our findings suggested that bashi's annotation quality may need to be reassessed.