Exophoric Pronoun Resolution in Dialogues with Topic Regularization (2021)

Abstract: Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario. Exophoric pronouns are common in daily communications, where speakers may directly use pronouns to refer to some objects present in the environment without introducing the objects first. Although such objects are not mentioned in the dialogue text, they can often be disambiguated by the general topics of the dialogue. Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem. Extensive experiments demonstrate the effectiveness of adding topic regularization for resolving exophoric pronouns.

## introduction grounding pronouns to objects they refer to is a challenging yet crucial natural language understanding problem. the coreference relationship between a pronoun and its referents is categorized into endophora and exophora based on whether the referred objects appear in text or out of text, and the former case can be further divided into anaphora if the referents appear in the preceding text of the pronoun and cataphora if in the following text @xcite . conventional studies on the pronoun coreference resolution (pcr) task in the nlp community mainly focus on anaphora @xcite @xcite and some recent work analyzes cataphora in machine translation @xcite , while mostly ignoring the exophoric pronouns. however, in daily dialogues or conversations, speakers may often use exophoric pronouns to refer to objects in the situational context that all speakers and listeners are aware of without introducing them in the first place. meal √ topic: cooking and eating cleaning service? i want to cook but the kitchen is in a mess. when is the house cleaning service scheduled? it is scheduled at 8 p.m.. well, i am starving right now. could you order that like last friday? figure 1 : an example of resolving exophoric pronouns in daily dialogues with and without the help of dialogue topics. this limits the use of current pcr models in many real-world dialogue/conversation scenarios, e.g., text interpretation @xcite and downstream tasks such as dialogue generation @xcite . figure 1 shows an example of exophora. a person talks with his ai assistant (siri/alexa), "could you order that like last friday?" in this scenario, "that" is an exophoric pronoun whose referent can not be found in the dialogue text. a smart enough ai system should be able to resolve the pronoun "that" to some food rather than cleaning service based on the context. such resolution of exophora is a crucial step in natural language understanding for the ai dialogue system to generate meaningful and relevant responses. since traditional pcr tasks only focus on endophoric pronouns while ignoring exophoric ones, all existing models struggle when the correct referent is not in the textual context of the target pronoun. for example, most of the human-defined rules @xcite (e.g., "them" can only refer to plural objects) and features @xcite (e.g., the distance between the target pronoun and candidate noun phrase) become either less effective or inapplicable in the exophoric setting. unlike humandesigned patterns or feature-based methods, the end-to-end coreference models @xcite have the potential of resolving pronouns to external objects as long as the names of objects are provided as candidates. nonetheless, these models heavily rely on the representation of local context produced by deep models so they always tend to resolve pronouns to the mentions in near text. as figure 1 shows, the models could easily be distracted by the noun phrase "cleaning service" in text and resolve "that" to the service. to address the limitations of current models, we propose to take the overall dialogue topics into consideration. for the example in figure 1 , we can judge from the whole dialogue that the topic is about cooking and eating, so it is likely that the person needs some food. if the ai system correctly resolves "that" to the topic-related out-of-text object "meal," this may help the ai assistant to finally give a reasonable response, "i will order the takeaway that you had last friday." to quantitatively define and evaluate exophora resolution, we leverage the vispro dataset @xcite , which annotates pcr information on visual dialogues. it is the only pcr dataset with annotations of out-of-text referents to the best of our knowledge. while the original dataset provides images alongside dialogues, we observe that humans can resolve 96% of exophoric pronouns in vispro with only dialogue texts, which perfectly matches our research goal. therefore, we perform out-of-text pcr experiments on the texts of vispro. in this paper, we define the out-of-text pcr task and present a model, which jointly leverages the local context and global topics to better resolve pronouns to out-of-text objects. the model first identifies the overall dialogue topics and then assign larger scores to objects which are more relevant to the topics. by doing so, it less overfits the local context and learns to resolve pronouns based on global topics. experimental results prove that the proposed model can significantly boost the performance of resolving exophoric pronouns without sacrificing the performance on in-text pcr. we also conduct an extensive analysis to show the contribution of different components. the data, code, and models are available at: https://github. com/hkust-knowcomp/exo-pcr . ## related works coreference resolution is the task of identifying coreference relations among different mentions. as a vital natural language understanding component, a good coreference system could benefit many downstream tasks such as machine translation @xcite , dialog systems @xcite ), question answering @xcite , and summarization @xcite . due to the weak semantic meaning of pronouns @xcite , grounding pronouns to their referents (pcr) has been specially studied as a more challenging task than the general coreference resolution @xcite . previous pcr studies @xcite mostly focus on resolving pronouns to mentions in the near context. however, in informal text such as daily dialogues, it is common that pronouns may refer to out-of-text objects, which is crucial for dialogue understanding. such pronouns have long been discussed as "pragmatically controlled anaphora" in linguistics @xcite @xcite , but there has been few discussion of exophoric pronouns in the nlp community. @xcite deal with exophora of zero pronouns, a special phenomenon in japanese where an omitted argument of a predicate might refer to the "author" or the "reader" of the document. @xcite qualitatively analyze exophoric reference in twitter conversations, where the antecedent of a pronoun could appear in the attached media or the quoted tweet. unlike previous works, we follow a more general linguistics definition of exohpora @xcite and evaluate it quantitatively. one recent work @xcite annotates a dataset vispro containing in-text and out-of-text referents for pronouns in visual dialog @xcite , and solve the pcr task by involving visual information. in this work, we propose to resolve exophora in vis-pro with texts as the only input. our model jointly uses local context and global topic information for exophora resolution, which does not require the support of visual signals and thus can be applied to all scenarios. ## the task in this section, we introduce details about the dataset construction and the task definition. ## the model the goal of the coreference model is to provide the coreference score f (p, d) between a pronoun p and a candidate d, which can either be a mention @xmath0. we divide the coreference score into three parts: the similarity score between p and d based on local context, the global topic relevance score of p, and that of d: specifically, f l calculates the similarity between p and d via local context representations, while f g acquires the relevance score between each text span and the global topics. to capture the topic information of the dialogues, we employ topic prediction as an auxiliary task of the model. the overall model architecture is shown in figure 3 and details are as follows. ## the experiment in this section, we introduce the experiment details. ## the results from the experimental results in further analyzing the performances of models on out-of-text pcr, we observe that the "not dis-