Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations (2022)

Abstract: This paper suggests a direction of coreference resolution for online decoding on actively generated input such as dialogue, where the model accepts an utterance and its past context, then finds mentions in the current utterance as well as their referents, upon each dialogue turn. A baseline and four incremental updated models adapted from the mention linking paradigm are proposed for this new setting, which address different aspects including the singletons, speaker-grounded encoding and cross-turn mention contextualization. Our approach is assessed on three datasets: Friends, OntoNotes, and BOLT. Results show that each aspect brings out steady improvement, and our best models outperform the baseline by over 10%, presenting an effective system for this setting. Further analysis highlights the task characteristics, such as the significance of addressing the mention recall.

## introduction it has been made practical recently to apply coreference resolution to assist a broad scope of nlp tasks @xcite @xcite , especially with the advent of neural end-to-end decoding and contextualized encoding @xcite @xcite @xcite . however, it is quite limited to use existing coreference models in real-time dialogue processing systems, as most of them are not trained to handle an online decoding environment. in the dialogue domain, recent efforts have focused on ellipsis recovery and query rewriting @xcite ; in this work, we target to address a new perspective specifically for the online decoding, where the model sequentially accepts utterances in a dialogue and spits out valid mentions as well as their referent links for each latest utterance turn upon arrival, to be consumed by the downstream dialogue processing (figure 1 ). more formally, let u i be the current (i'th) utterance in a dialogue (u 1 , .., u i , ..); m i be the mentions in u i ; m i-1 be the mentions from previously predicted clusters till u i-1 . the objective upon i'th turn is to: (1) identify m i (2) identify conference links among m i , as well as from m i to m i-1 . we do not allow updates on m i later, since that would be equivalent to general coreference resolution; in this work, we specifically target this underexplored online scenario under this setting, which requires accurate predictions upon each turn that could be directly consumed by downstream applications. several quasi-online coreference models have been proposed that maintain and update referents sequentially @xcite @xcite @xcite . however, these models differ from our real online setting in two ways. first, only the latest utterance and its past sequence are visible in our setting, so that decisions need to be made without knowing the unseen future. second, the decision of whether a span should be extracted or linked to others needs to be made immediately at each utterance turn, while quasi-online models can maintain an internal pool of candidates and make one final prediction after the entire document is processed. for this task, we first introduce our baseline adapted from the classic mention-linking (ml) ap-proach @xcite , and then propose four models where each one does an incremental update upon the previous model and addresses a specific perspective of this task, including the online inference, singletons, speakergrounded encoding, and mention contextualization across utterance turns. for our approach, we do not use models that maintain explicit entities, because: (1) it has been shown that higher-order features from entity representation provide negative to marginal positive impact over ml counterparts despite their complexities @xcite @xcite ; (2) ml models are "stateless" so that they do not need to maintain decision states for previous mentions, which makes it more adaptable to applications in practice. all models are evaluated on three datasets to test the generalizability of our approach, and the best model obtains over 10% improvement over the baseline on all datasets. results and further analysis suggest that each aforementioned aspect can bring out steady improvement under the online setting, and highlight the singleton recovery to be the most critical component. ## approach end-to-end resolution our model backbone is based on the end-to-end coreference resolution @xcite with a transformers encoder @xcite . it scores every span for being a mention, and extracts top spans as mention candidates. pairwise scoring is then performed among all candidates to determine the coreference links. details of the model architecture can be referred by the paper from @xcite , and we denote the original coreference loss as l c . ## experiments datasets all models are experimented on the following three datasets. friends contains transcripts from the tv show in which personal mentions are annotated for entity linking. each scene is considered an independent dialogue where utterances and speaker ids are provided. we adapt the data split suggested by @xcite . onto-conv consists of documents in three genres selected from ontonotes 5.0: broadcasting and telephone conversations, and web text including discussion forums. we adapt the data split provided by @xcite and treat each document as a dialogue and every sentence as an utterance. bolt follows the same annotation guideline as ontonotes although documents are from discussion forums, sns chats, and telephone conversations @xcite . since this is the first work using bolt for this task, we create a new data split for future replicability (see a.1). out of these three datasets, only friends provides annotation of singletons. the numbers of documents in the training, development, and test set of friends, onto-conv, bolt are provided in settings our implementation are based on the pytorch coreference models from @xcite , and spanbert base is adopted as the encoder. the implementation and trained models during inference, all predicted clusters are collected and merged accordingly across utterances, and get evaluated by comparing them to the ground truth (all gold non-singleton clusters) at the end of each dialogue, in the same way as the conll'12 shared task protocol. detailed experimental settings are provided in appendix a.2. results ## conclusion this paper presents a new coreference resolution direction that aims towards an online decoding setting for dialogue processing. a baseline and four incremental-updated models are proposed and evaluated on three datasets of the dialogue domain, and the best-performing model shows significant improvement over the baseline by ≈10% f1. further analysis suggests the importance of mention recall and speaker encoding, which could serve as the next future directions of this online setting.