End-to-End Neural Discourse Deixis Resolution in Dialogue (2022)

Abstract: We adapt Lee et al.’s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.

## introduction discourse deixis (dd) resolution, also known as abstract anaphora resolution, is an under-investigated task that involves resolving a deictic anaphor to its antecedent. a deixis is a reference to a discourse entity such as a proposition, a description, an event, or a speech act @xcite . dd resolution is arguably more challenging than the extensivelyinvestigated entity coreference resolution task. recall that in entity coreference, the goal is to cluster the entity mentions in narrative text or dialogue, which are composed of pronouns, names, and nominals, so that the mentions in each cluster refer to the same real-world entity. lexical overlap is a strong indicator of entity coreference, both among names (e.g., "president biden", "joe biden") and in the resolution of nominals (e.g., linking "the president" to "president biden"). dd resolution, on the other hand, can be viewed as a generalized case of event coreference involving the clustering of deictic anaphors, which can be pronouns or nominals, and clauses, such that the mentions in each cluster refer to the same real-world proposition/event/speech act, etc. the first example in figure 1 is an example of dd resolution in which the deictic anaphor "the move" refers to salomon's act of issuing warrants on shares described in the preceding sentence. dd resolution is potentially more challenging than entity coreference resolution because (1) dd resolution involves understanding clause semantics since antecedents are clauses, and clause semantics salomon brothers international ltd. announced it will issue warrants on shares of hong kong telecommunications ltd. the move closely follows a similar offer by salomon of warrants for shares of hongkong & shanghai banking corp. a: would you donate to save the children? b: yes, i will do @xmath010. while the deictic anaphors in dialogue are also composed of pronouns and nominals, the proportion of pronominal deictic anaphors in dialogue is much higher than that in narrative text. for instance, while 76% of the deictic anaphors in two text corpora (arrau rst and gnome) are pronominal, the corresponding percentage rises to 93% when estimated based on seven dialogue corpora (trains91, trains93, pear, and the four codi-crac 2021 development sets). in fact, the three pronouns "that", "this", and "it" alone comprise 89% of the deictic anaphors in these dialogue corpora. the higher proportion of pronominal deictic anaphors potentially makes dd resolution in dialogue more challenging than those in text: since a pronoun is semantically empty, the successful resolution of a pronominal deictic anaphor depends entirely on proper understanding of its context. in addition, it also makes dd recognition more challenging in dialogue. for instance, while the head of a non-pronominal phrase can often be exploited to determine whether it is a deictic anaphor (e.g., "the man" cannot be a deictic anaphor, but "the move" can), such cues are absent in pronouns. since dd resolution can be cast as a generalized case of event coreference, a natural question is: how successful would a state-of-the-art entity coreference model be when applied to dd resolution? recently, @xcite have applied @xcite re-implementation of lee et al.'s span-based entity coreference model to resolve the deictic anaphors in the dd track of the codi-crac 2021 shared task after augmenting it with a type prediction model (see section 4). not only did they achieve the highest score on each dataset, but they beat the second-best system @xcite , which is a non-span-based neural approach combined with hand-crafted rules, by a large margin. these results suggest that a spanbased approach to dd resolution holds promise. our contributions in this paper are three-fold. first, we investigate whether task-specific observations can be exploited to extend a span-based model originally developed for entity coreference to improve its performance for end-to-end dd resolution in dialogue. second, our extensions are effective in improving model performance, allowing our model to achieve state-of-the-art results on the codi-crac 2021 shared task datasets. finally, we present an empirical analysis of our model, which, to our knowledge, is the first analysis of a state-of-the-art span-based dd resolver. ## related work broadly, existing approaches to dd resolution can be divided into three categories, as described below. rule-based approaches. early systems that resolve deictic expressions are rule-based @xcite @xcite . specifically, they use predefined rules to extract anaphoric mentions, and select antecedent for each extracted anaphor based on the dialogue act types of each candidate antecedent. non-neural learning-based approaches. early non-neural learning-based approaches to dd resolution use hand-crafted feature vectors to represent mentions @xcite . a classifier is then trained to determine whether a pair of mentions is a valid antecedent-anaphor pair. deep learning-based approaches. deep learning has been applied to dd resolution. for instance, @xcite and @xcite use a siamese neural network, which takes as input the embeddings of two sentences, one containing a deictic anaphor and the other a candidate antecedent, to score each candidate antecedent and subsequently rank the candidate antecedents based on these scores. in addition, motivated by the recent successes of transformer-based approaches to entity coreference (e.g., @xcite ), @xcite have recently proposed a transformer-based approach to dd resolution, which is an end-to-end coreference system based on spanbert @xcite . their model jointly learns mention extraction and dd resolution and has achieved state-of-the-art results in the codi-crac 2021 shared task. ## corpora we use the dd-annotated corpora provided as part of the codi-crac 2021 shared task. for training, we use the official training corpus from the shared task @xcite , arrau @xcite , which consists of three conversational sub-corpora (trains-93, trains-91, pear) and two non-dialogue sub-corpora (gnome, rst). for validation and evaluation, we use the official development sets and test sets from the shared task. the shared task corpus is composed of four well-known conversational datasets: ami @xcite , light @xcite , persuasion @xcite , and switchboard @xcite . statistics on these corpora are provided in ## baseline systems we employ three baseline systems. the first baseline, coref-hoi, is xu and choi's (2020) re-implementation of @xcite widely-used end-to-end entity coreference model. the model ranks all text spans of up to a predefined length based on how likely they correspond to entity mentions. for each top-ranked span x, the model learns a distribution p (y) over its antecedents @xmath1, where y(x) includes a dummy antecedent ϵ and every preceding span: to the same entity 1 (s a (x, ϵ) = 0 for dummy antecedents): the second baseline, utd_nlp 2 , is the topperforming system in the dd track of the codi-crac 2021 shared task @xcite . it extends coref-hoi with a set of modifications. two of the most important modifications are: (1) the addition of a sentence distance feature to ϕ(•), and (2) the incorporation into coref-hoi a type prediction model, which predicts the type of a span. the possible types of a span i are: antecedent (if i corresponds to an antecedent), anaphor (if i corresponds to an anaphor), and null (if it is neither an antecedent nor an anaphor). the types predicted by the model are then used by coref-hoi as follows: only spans predicted as anaphor can be resolved, and they can only be resolved to spans predicted as antecedent. details of how the type prediction model is trained can be found in @xcite . the third baseline, coref-hoi-utt, is essentially the first baseline except that we restrict the candidate antecedents to be utterances. this restriction is motivated by the observation that the 1 see @xcite for a description of the differences between sc(•) and sa(•), ## model next, we describe our resolver, dd-utt, which augments coref-hoi-utt with 10 extensions. e1. modeling recency. unlike in entity coreference, where two coreferent names (e.g., "joe biden", "president biden") can be far apart from each other in the corresponding document (because names are non-anaphoric), the distance between a deictic anaphor and its antecedent is comparatively smaller. to model recency, we restrict the set of candidate antecedents of an anaphor to be the utterance containing the anaphor as well as the preceding 10 utterances, the choice of which is based on our observation of the development data, where the 10 closest utterances already cover 96-99% of the antecedent-anaphor pairs. e2. modeling distance. while the previous extension allows us to restrict our attention to candidate antecedents that are close to the anaphor, it does not model the fact that the likelihood of being the correct antecedent tends to increase as its distance from the anaphor decreases. to model this relationship, we subtract the term γ 1 dist(x, y) from s(x, y) (see equation ( 1 )), where dist(x, y) is the utterance distance between anaphor x and candidate antecedent y and γ 1 is a tunable parameter that controls the importance of utterance distance in the resolution process. since s(x, y) is used to rank candidate antecedents, modeling utterance distance by updating s(x, y) will allow distance to have a direct impact on dd resolution. ## conclusion we presented an end-to-end discourse deixis resolution model that augments @xcite span-based entity coreference model with 10 extensions. the resulting model achieved state-ofthe-art results on the codi-crac 2021 datasets. we employed a variant of this model in our recent participation in the discourse deixis track of the codi-crac 2022 shared task @xcite and achieved the best results (see @xcite for details). to facilitate replicability, we make our source code publicly available. 4 limitations below we discuss several limitations of our work. generalization to corpora with clausal antecedents. as mentioned in the introduction, the general discourse deixis resolution task involves resolving a deictic anaphor to a clausal antecedent. the fact that our resolver can only resolve anaphors to utterances raises the question of whether it can be applied to resolve deictic anaphors in texts where antecedents can be clauses. to apply our resolver to such datasets, all we need to do is to expand the set of candidate antecedents of an anaphor to include those clauses that precede it. while corpora annotated with clausal antecedents exist (e.g., trains-91 and trains-93), we note that the decision made by the codi-crac 2021 shared task organizers to use utterances as the unit of annotation has to do with annotation quality, as the interannotator agreement on the selection of clausal antecedents tends to be low @xcite , discourse deixis resolution in dialogue vs. narrative text. whether our model will generalize well to non-dialogue datasets (e.g., narrative text) is unclear. given the differences between dialogue and non-dialogue datasets (e.g., the percentage of pronominal anaphors), we speculate that the performance of our resolver will take a hit when applied to resolving deictic anaphors in narrative text. size of training data. we believe that the performance of our resolver is currently limited in part by the small amount of data on which it was trained. the annotated corpora available for training a discourse deictic resolver is much smaller than those available for training an entity coreference resolver (e.g., ontonotes @xcite ).