Neural Anaphora Resolution in Dialogue Revisited (2022)

Abstract: We present the systems that we developed for all three tracks of the CODI-CRAC 2022 shared task, namely the anaphora resolution track, the bridging resolution track, and the discourse deixis resolution track. Combining an effective encoding of the input using the SpanBERT _{\text{Large}} encoder with an extensive hyperparameter search process, our systems achieved the highest scores in all phases of all three tracks.

## introduction following the codi-crac 2021 shared task @xcite , the codi-crac 2022 shared task @xcite focuses on resolving anaphoric references in dialogue. the two shared tasks are structured in more or less the same way. specifically, in order to track progress on resolving anaphoric references in dialogue made over the past year, this year's shared task has essentially the same format as last year's, except that a new gold anaphor (gold a) phase is added to both the bridging resolution track and the discourse deixis resolution track. by providing the participants with gold anaphors, the gold a phase allows participants to focus on antecedent selection, thus making it easier to compare different systems' resolution performances. similar to last year, this year we participated and ranked first in all phases of all three tracks. we believe that our success can largely be attributed to two factors. first, leveraging the successes achieved by span-based models in last year's shared task @xcite , we employ spanbert large @xcite as our encoder in all three tracks. second, we combine the resulting effective encoding of the input documents with an extensive hyperparameter search process. more specifically: a brief overview of the approaches we adopted for the three tracks can be found in the rest of the paper is structured as follows. the next three sections describe our work for the three tracks, namely entity coreference (section 2, discourse deixis (section 3), and bridging (section 4). in each section, we describe our approach, our official results, and a brief analysis of the results, particularly a discussion of the impact of hyperparameter tuning on model performance. finally, we present our conclusions in section 5. ## anaphora resolution last year we built a span-based entity coreference model for the anaphora resolution track that achieved competitive performance @xcite . since this year's anaphora resolution track has the exact same format as last year's, we developed this year's model based on last year's model (henceforth utd 2021 ). recall that utd 2021 is an extension of @xcite entity coreference resolution baseline @xcite implementation of @xcite span-based model learning framework a pipeline architecture consisting of a mention detection component, an entity coreference component, and a non-entity and non-referring mention removal component. the coreference component extends the baseline by (1) removing the type prediction model; and (2) rescoring the dummy antecedent at inference time to adjust the likelihood it will be selected as the antecedent. markable extraction a mention detection model (adapted from @xcite ) is trained to identify the entity mentions. training data the first two steps of our pipelined approach are pretrained on ontonotes 5.0. all three steps of our pipelined approach are trained on arrau 3.0 (rst, gnome, trains91, trains93, pear, lighttrain, amitrain, persuasiontrain, switchboardtrain). development data for all three steps, lightdev, amidev, persuasiondev, and switchboarddev are used. note that after parameters are tuned on the dev data, we retrain the models on the combined training and dev sets using the tuned parameters before continuing parameter tuning on the test data. see section 2.4.3 for details. ## discourse deixis resolution the discourse deixis track in this year's shared task has three evaluation phases: (1) the predicted phase, where a system needs to extract both antecedents and anaphors and perform discourse deixis resolution; (2) the gold mention phase, which is the same as the predicted phase except that anaphors are to be extracted from the given set of gold mentions; and (3) the gold anaphor phase, which is the same as the gold mention phase except that gold anaphors are explicitly given. the gold anaphor phase is introduced in this year's shared task to partially address the difficulty of comparing different resolvers with respect to their resolution performance @xcite . ## bridging resolution like the discourse deixis track, the bridging resolution track in this year's shared task has three different phases, namely the predicted phase, the gold mention phase, and the gold anaphor phase. while discourse deixis resolution has received fairly little attention in the nlp community in recent years, constant progress has been made for bridging resolution. nevertheless, such progress has thus far limited to a large extent to the gold mention setting, where gold mentions are given, and the gold anaphor setting, where gold anaphors are given (see @xcite for a comprehensive overview and @xcite for state-of-the-art results). in particular, little progress has been made on end-to-end bridging resolution, which corresponds to the setup used in the predicted phase of the shared task. motivated in part by the success of the hybrid rule-based and learning-based approach to bridging resolution developed by @xcite , we adopted a multi-pass sieve approach to bridging resolution in last year's shared task, where we employed a pipeline of sieves consisting of a neural sieve, which is essentially yu and poesio's spanbased neural model that employs multi-task learning, and a set of same-head sieves, which were specifically designed to target the identification of bridging links between two mentions having the same head. given that the improvement offered by the same-head sieves is small, we abandon them this year and focus instead on extending yu and poesio's multi-task learning framework for bridging resolution. below we first provide an overview of yu and poesio's model. ## conclusions we presented the systems that we developed for all three tracks of the codi-crac 2022 shared task, namely the anaphora resolution track, the bridging resolution track, and the discourse deixis resolution track. for anaphora resolution, we employed a three-step approach consisting of mention extraction, coreference resolution, and removal of non-referring and non-entity mentions. our results demonstrated that the third-step model, the nonreferring/non-entity removal model, contributed a lot to overall resolution performance. however, our system is still not able to handle split-antecedents, which is a direction for future improvements. for discourse deixis resolution, our results revealed that one of the key weaknesses in our system is anaphor detection, as a large performance gain could be achieved when the model was applied to gold anaphors. for bridging resolution, our results showed that the gold anaphor phase was much easier than the predicted phase and the gold mention phase. the resulting large performance gap provided suggestive evidence that there is still a lot of room for improvement in bridging anaphor detection. future work should focus on (1) determining the extent to which performance would deteriorate when all model parameters are tuned on development data and (2) performing a cross-team analysis to better understand how the resolvers from different teams are different from each other.