Anaphora Resolution in Dialogue: System Description ( CODI - CRAC 2022 Shared Task) (2022)

Abstract: We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the “cluster merging” version of the coref-hoi model, which brings up to 10.33% improvement1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of coref-hoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.

## introduction in this paper we present our systems submitted for the codi-crac 2022 shared task (ccst) on anaphora, bridging, and discourse deixis in dialogue foot_1 @xcite . the task is a follow-up to the one held last year and described in @xcite . as its name suggests, besides identity anaphora this shared task tries to cover other, less-studied, anaphoric phenomena, and offers new multi-genre data that combines several types of annotations in universal anaphora foot_2 format. main focus of the shared task is on dialogue. dialogue data offers new challenges, like grammatically incorrect utterances, disfluencies, more deictic references, speaker grounding and long-distance conversation structure @xcite . while coreference resolution in text has been very actively studied in the recent years, it is much less researched in dialogue, especially such forms as bridging, or discourse deixis. descriptions of early systems implemented for the resolution of 'standard' and discourse deictic pronouns in dialogue can be found, e.g., in @xcite , @xcite , @xcite . more approaches (not implemented), together with some useful findings are presented, e.g., in @xcite , @xcite , @xcite . ccst 2021 stirred new interest in coreference resolution in dialogue. the majority of systems submitted for it represent various modifications of either the higher-order coreference resolution model (coref-hoi) by @xcite , or one of the earlier models by @xcite and @xcite . these models were originally trained on the text data, and are span-based -each span gets associated with a score, and anaphor-antecedent pairs are established based on the pairwise scores. designed for identity anaphora resolution, these models were also adapted for bridging and discourse deixis resolution. examples of span-based models submitted for ccst 2021 include systems by @xcite , @xcite , @xcite . other participants presented different approaches. thus, @xcite perform identity anaphora and bridging resolution using pointer networks. @xcite cast anaphora resolution as a clustering problem, and discourse deixis resolution -as a siamese net based scoring function. inspired by the success of the span-based coreference resolution models, we submit three independent systems for ccst 2022. our system for identity anaphora resolution uses both the workspace coreference system by @xcite and the coref-hoi model as described in section 2. the model for discourse deixis extends coref-hoi with shallow linguistic features and aims at resolving three types of potential anaphors. it is described in section 3. the model for bridging resolution is a modification of the system by @xcite . the approach is explained in section 4. ## anaphora resolution for the anaphora resolution track we trained and combined the outputs of the workspace coreference system (wcs) and the coref-hoi system (see ## discourse deixis resolution ccst 2022 offers three different tracks for discourse deixis resolution. first track (eval-dd pred) assumes finding antecedents for discourse deixis anaphors predicted by models given unannotated data. the second one (eval-dd gold m) aims at identification of discourse deixis anaphors among all types of annotated anaphors and non-referential mentions, and their subsequent resolution. the goal of the last track (eval-dd gold a) is to find antecedents for already annotated discourse deixis anaphors. our team participated in all three tracks. the core of our approach relies on the corefhoi model, because it was successfully adopted for ccst 2021 discourse deixis track by @xcite . their model was able to achieve the conll f1 score of 35.4% -52.1% depending on the dataset and shared task track, and ranked first for discourse deixis @xcite . the summary of our system can be found in ## bridging resolution in this section we introduce our submission for the resolution of bridging references. we submitted to the eval-br (gold a) track, in which gold mentions and anaphors are given. this reduces the problem to the selection of antecedent (from gold mention candidates) for each given anaphor. ## conclusion and outlook in this paper we presented our systems for identity anaphora, bridging and discourse deixis resolution. our system for the identity anaphora resolution combines the outputs of wcs and the coref-hoi system trained with "cluster merging". it ranked second in the shared task competition. when experimenting with wcs we tested different settings and tried replacing and adding different embeddings for mention representations (e.g., spanbert). however, the configuration reported in @xcite turned out to work best on our development set. we also tested a combination of wcs trained on the shared task data and ccs trained on ontonotes as well as coref-hoi trained on a combination of dialogue and non-dialogue datasets. the analysis of the model outputs shows that wcs works reasonably well for detecting singletons and pronominal clusters but performs worse when clustering noun phrases. hence, we combine the outputs of wcs and the coref-hoi model and achieve an average improvement of 7.95% conll score over vanilla clustering with wcs. in the future we would like to do a more fine-grained analysis of the combined model outputs and test if one could use automatic coreference annotations from other pre-trained models as a weak supervision signal for wcs. in particular, we are interested in evaluating this model on the domain adaptation task and in the low resource setting. we would also like to perform more experiments with coreference chain editing based on the outputs of several models. the system for discourse deixis resolution ranked second for all three tracks of the shared task. it was able to reach the conll f1 scores ranging from 35.91% to 62.79% depending on the track and dataset. some of these scores are close to the scores achieved by the winning team. the model is based on a novel idea that it is possible to combine the tasks of discourse deixis and anaphora resolution. it is our first attempt at implementing this idea, so there is much space for improvement and additional analysis. first, we plan on making our model computationally more efficient, namely, we are going to perform some experiments with adaptive span pruning and check the influence of linguistic features given a larger training set. second, it is possible to expand the set of potential anaphors. before doing that, we need to analyse the ability of our model to resolve identity anaphora. depending on the results, we may use our discourse deixis resolution model to enhance the coreference resolution performed by the wcs model. finally, the phenomenon of split antecedents requires more investigation, namely, how we can model coherence/relations between them. the system for the resolution of gold bridging anaphors is based on a higher order coreference system adapted for the setting. while the gold mentions/anaphors setting is much simpler than full bridging (mention/anaphor detection and resolution), the results show how well the span embedding and pairwise scoring mechanisms from @xcite and @xcite work for bridging pairs.