End-to-End Neural Bridging Resolution (2022)

Abstract: The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution. In light of these problems, we evaluate bridging resolvers in an end-to-end setting, strengthen them with better encoders, and attempt to gain a better understanding of them via perturbation experiments and a manual analysis of their outputs.

## introduction bridging was used by @xcite to refer to nonidentity relations between anaphoric noun phrases (i.e., bridging anaphors) and their antecedents. in example 1, "ruling -party members" is a bridging anaphor and its antecedent is "japan". bridging resolution is the task of identifying bridging anaphors and linking them to their antecedents. many tasks can benefit from bridging resolution, such as textual entailment @xcite and question answering @xcite . bridging resolution is arguably less studied but more challenging that entity coreference resolution, the task of determining which entity mentions refer to the same entity in the real world. specifically, while linguistic constraints on coreference exist at the grammatical (e.g., gender and number agreement), syntactic (e.g., c-command), and semantic (e.g., semantic type agreement) levels that can be used to filter candidate antecedents, such constraints are largely absent for bridging resolution. for instance, a singular bridging anaphor (e.g., "the book") can refer to a plural antecedent (e.g., "books"), and bridging relations can be formed from mentions with different entity types (e.g., "the house" and "the window"). in fact, while many coreference relations can be identified via string matching facilities, it is not uncommon for bridging relations to be identified using background knowledge and/or sophisticated inference mechanisms. the complexity of bridging resolution is further complicated by the lack of a large corpus annotated with bridging relations: while the most extensively-used coreference-annotated corpus, ontonotes, contain more than 2000 documents, two of the most commonly-used corpora for bridging resolution, isnotes and bashi, each contains only 50 documents taken from ontonotes. the current state of bridging resolution research is rather unsatisfactory. state-of-the-art bridging resolvers are typically evaluated in unrealistic settings: in the bridging anaphora resolution task, the goal is to identify the antecedent of a given bridging anaphor; and in the full bridging resolution task, the goal is to first identify the bridging anaphors given a set of gold mentions and then resolve each anaphor to its antecedent @xcite ). while such unrealistic settings have been considered unacceptable for evaluating entity coreference resolvers for more than a decade, they are still extensively used to evaluate bridging resolvers nowadays simply because the end-to-end setting, where a resolver needs to identify bridging relations given a raw document, is perceived to be overly challenging. worse still, while bridging resolution is more challenging than coreference resolution, models for bridging resolution are less sophisticated than those for coreference resolution. for instance, while spanbert, a version of bert specifically pre-trained to identify text spans @xcite , has been used successfully as an encoder in span-based entity coreference models, the state-of-the-art neural bridging resolver developed by @xcite simply uses a bidirectional lstm to encode the input document. our goal in this paper is to gain a better un-derstanding of the state of the art in bridging resolution. first, we conduct a systematic evaluation of bridging resolvers in an end-to-end setting. in particular, we focus on evaluating three stateof-the-art approaches, including a rule-based approach by @xcite , a neural approach by @xcite , and a hybrid rule-based and learning-based approach by @xcite , showing how each of them can be extended so that they can be applied in an end-to-end setting. next, we strengthen @xcite neural model by replacing the bilstm encoder it uses with stronger encoders such as bert @xcite and spanbert @xcite . these experiments can help us determine whether the commonsense knowledge encoded in these pretrained language models can be profitably exploited for bridging resolution, which could be important given that state-of-the-art bridging resolvers are trained on small annotated corpora. further, to better understand the extent to which a bridging resolver relies on certain words/phrases in the input, we conduct perturbation experiments. finally, to complement the quantitative analysis in our experiments, we conduct a qualitative analysis of the outputs produced by our best-performing resolver. our contributions in this paper are three-fold. first, our experiments reveal that end-to-end bridging resolution is not as challenging as typically perceived: for the most part, end-to-end bridging resolution lags behind its "gold mention" counterpart by less than 3 points in absolute f-score. these results provide suggestive evidence that time is ripe for abandoning unrealistic evaluations of bridging resolvers. second, we establish baseline results for end-to-end bridging resolution against which future work can be compared on two commonly-used referential bridging corpora, isnotes and bashi. while our evaluations have largely focused on the end-to-end setting, for comparison purposes we also present evaluation results in the "gold mention" setting, in which our strongest models achieve state-of-the-art results on isnotes and bashi. ## related work many previous computational studies on bridging have focused on one of the two sub-tasks of bridging resolution, namely bridging anaphora recognition and bridging anaphora resolution (see @xcite for a comprehensive overview of this area of research). bridging anaphora recognition has been tackled as part of the information status (is) classification problem @xcite @xcite . recall that the goal of is classification is to assign an is to each discourse entity that indicates how these entities are referred to in a text @xcite @xcite : an entity is old if it is coreferent with an entity that has been mentioned before, new if it is introduced into the discourse for the first time and is not known to the hearer before, and mediated if it has not been introduced in the discourse but can be inferred from previously mentioned entities. bridging anaphors are a type of mediated entities that are discourse-new but hearer-old. bridging anaphora resolution, on the other hand, focuses on selecting antecedents for bridging anaphors @xcite @xcite . there are a few works tackling full bridging resolution (i.e., recognizing bridging anaphors and linking them to the antecedents), ranging from rule-based approaches @xcite , to machine learning-based approaches @xcite and hybrid methods @xcite . however, these resolvers all assume that gold mentions are given, which hinders the application of bridging resolution in downstream tasks. in contrast, we focus on end-to-end bridging resolution. note that some recent attempts have been made in this direction. for example, @xcite approach to bridging anaphora resolution does not require gold mentions when constructing the list of antecedent candidates; nevertheless, it still needs gold bridging anaphora information. in addition, while @xcite proposes an end-to-end neural approach to the related tasks of is classification and bridging anaphora recognition, it has not been extended to bridging resolution. more recently, in the bridging track of the codi-crac shared task on anaphora, bridging, and discourse deixis in dialogue in 2021 @xcite and 2022 @xcite , the participants built resolvers for performing end-to-end bridging resolution in dialogue in the "predicted" phase @xcite @xcite . ## state-of-the-art approaches existing approaches to bridging resolution can be broadly divided into rule-based approaches, learning-based approaches, and hybrid approaches. ## conclusion we conducted a pioneering study on end-to-end neural bridging resolution in which we adapted three state-of-the-art bridging resolvers that were originally developed to operate on gold mentions, namely @xcite @xcite resolver, to the end-to-end setting. to strengthen the resolvers, we replaced the lstm encoders they use with bert-and spanbertbased encoders. in an evaluation on isnotes and bashi, end-to-end bridging resolvers lagged behind their gold-mention counterparts by only 2-3% absolute f-score. these results suggested that time is ripe for researchers to focus on evaluating bridging resolvers in the end-to-end setting. in addition, our work suggested that h 2 (sbert p ), the hybrid score-based pipeline bridging resolver trained using sbert (1) achieves better performance than other model variants; (2) is sensitive to all kinds of perturbations we considered; and (3) will likely be improved by improving mediated/syntactic errors.