An End-to-End Approach for Full Bridging Resolution (2021)

Abstract: In this article, we describe our submission to the CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues – Track BR (Gold). We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging. We find that while our approach is not effective at modeling the complexities of the task, it performs well on bridging resolution, suggesting a need for investigations into a robust anaphor identification model for future improvements.

## introduction anaphora is a discourse level phenomenon wherein a linguistic entity (referred to as anaphor) is associated with some other linguistic entity (referred to as an antecedent) within a document @xcite . broadly, the phenomenon is divided into coreference and bridging anaphora depending on whether the anaphoric references are linked to associated antecedents with an identical (is-a), or a non-identical (¬ is-a) relation, respectively. following is an example of bridging anaphora -the focus of this article: starbucks has a new take on the unicorn cappuccino. one employee accidentally leaked a picture of the secret new drink. here, the noun phrase "one employee" (bridging anaphor) is anaphorically linked to the antecedent -"starbucks". in this instance, bridging anaphor can be thought of as an expression with an implicit argument, i.e., one employee (of starbucks) @xcite . the task of full bridging is that of identifying anaphors from a given set of linguistic entities in 1 task home page - https://competitions. codalab.org/competitions/30312 the document and linking them with their nonidentical associated antecedent. this task is arguably more difficult and relatively understudied than that of entity coreference resolution -which involves identifying coreferent linguistic entities. this difficulty can be seen from the fact that bridging anaphors are less likely to conform to syntactic or surface clues @xcite , and the low inter-annotator agreement for bridging annotations @xcite . hindrances to rapid progress on the task, however, also include lack of ample gold labeled data @xcite , and standardised evaluation schemes @xcite . the codi-crac 2021 shared task @xcite is thus a welcome addition to the existing set of datasets in the field as it provides a consistent benchmark across multiple gold-annotated datasets. however, unlike the task variant that the majority of existing approaches tackle, the shared task also includes identification of the bridging anaphors as a part of the task. that is, most existing approaches @xcite @xcite assume the anaphor (here, "one employee") to be given, and are limited to selecting the correct antecedent for this phrase, amongst a predefined list of linguistic entities. the identification of anaphors compounds the difficulty of the task, owing to, amongst other things, the number of markables (linguistic entities) in a document (see that said, coreference resolution is a more complex task in terms of the decisions to be taken for correct predictions. coreference chains are variadic, whereas relations representing bridging anaphors are binary. further, contemporary approaches treat mention detection as a part of the task. the neural architectures thus proposed for coreference resolution are often more expressive than the task of full bridging (with gold markables) requires. based on this observation, we aim to find whether the aforementioned neural architectures can be adapted for solving a linguistically complex but simpler (in the above stated terms) task of full bridging. to that end, we experiment with the independent variant of the transformer based higher-order coreference model @xcite . we empirically find that the approach is inadequate in solving the task, and posit that the currently available amount of gold-labeled data is insufficient for this family of approaches. this suggests a need for more data, or the use of external information in solving the full bridging task when using this family of approaches. however, we observe that when tasked with only the resolution of bridging anaphors (i.e., the anaphoric markables are provided as a part of the task), this approach performs significantly better, suggesting that a twostep identification and resolution approach might be beneficial for the task. ## task description in this section, we introduce domain specific terms used throughout the article, and formalise the different variants of tasks corresponding to identification and association of bridging anaphors. we use the nomenclature used in @xcite . markables: a set of linguistic entities in a document of which anaphors and antecedents are both subsets. bridging anaphor: a markable whose interpretation depends upon an antecedent, or more generally, which is implicitly linked to an antecedent with a non-identity relation. we refer to them as simply anaphors in the rest of this article. antecedent: the markable which is related to the anaphor with an implicit non-identity relation. ## experiments in this first experiment, we perform full bridging (with gold mentions) over the provided datasets (sec. 4.1). ## conclusion in this work, we experiment with a higher-order coreference model based end-to-end approach for the full bridging task over conversational documents. we find that it is unable to model the task's complexities, however, its performance on bridging resolution is significantly better. this suggests a different approach to anaphor detection is needed, whether it be a stand alone anaphor detection model or a more guided adaptation of the higher-order coreference model @xcite that better suits the bridging task. we leave investigations along this line for the future. we also aim to experiment with approaches that can prime a model towards conversational documents, such as including the spanbert @xcite pretraining objective as a pre-finetuning step.