Toward Implicit Reference in Dialog: A Survey of Methods and Data (2022)

Abstract: Communicating efficiently in natural language requires that we often leave information implicit, especially in spontaneous speech. This frequently results in phenomena of incompleteness, such as omitted references, that pose challenges for language processing. In this survey paper, we review the state of the art in research regarding the automatic processing of such implicit references in dialog scenarios, discuss weaknesses with respect to inconsistencies in task definitions and terminologies, and outline directions for future work. Among others, these include a unification of existing tasks, addressing data scarcity, and taking into account model and annotator uncertainties.

## introduction in natural language conversations, speakers often leave out parts of the conversation which are understood by the other party through the shared context, as exemplified in figure 1 . this can either serve as a way to add variance to a conversation, to make the dialog more efficient by not repeating information, or to accomplish a specific conversational goal, such as displaying skepticism @xcite . these omissions can take the form of syntactically correct sentences that leave out important semantic information or even incomplete sentence fragments @xcite . figure 1 shows an example of a dialog which contains both of these. turn 2 demonstrates a syntactically correct sentence where the user asks about the capital, but leaves out which country they are referring to. turn 3 shows an example of a syntactically incomplete sentence, where the user leaves out both the country and the verbal phrase. in this paper we refer to these omitted entities as implicit references because while there is no direct reference, e.g., a pronoun, it is still understood that the user is referring to a specific entity. we propose turn utterance 1 usr who is the chancellor of germany? ## usr and what is the capital _? [of germany] sys the capital of germany is berlin. ## usr and _ the population _? [what is], [of germany] figure 1 : dialog between a user usr and a system sys, with examples of implicit references (in turn 2 and 3) and another implicit element (in turn 3) indicated by underscores in red. the correct resolution of each implicit element is shown in brackets in red. ## definitions in this section, we provide an overview of linguistic phenomena related to the concept of implicit reference and discuss overlaps and differences in definition. our focus lies on phenomena that occur in dialogue and written text involving an omitted element referring to an entity. ellipsis. ellipsis is a syntactic phenomenon in which a constituent is omitted because it can be resolved from the context. although there are multiple types of ellipsis, we limit the scope of this survey to focus only on nominal ellipsis. nominal ellipsis occurs when the head noun inside a noun phrase is implicit. an example taken from the noel corpus @xcite is: let's party at sam's np this friday. in this case, sam's location is omitted. when the noun phrase is fully omitted, noun ellipsis can also be seen as zero anaphora. therefore, some researchers use the term noun phrase ellipsis to refer to instances for which only a part of the noun phrase got deleted @xcite whereas others use the term to indicate both types of cases @xcite . ## implicit reference tasks in dialog this section introduces the most common areas of research on implicit references in dialog. ## data in the following subsections, we describe datasets which have been collected for studying implicit references in dialog. the datasets are directly compared in ## methods in the following section we outline methods which have been used for tasks related to implicit reference in dialogs. we provide an overview (figure 2 ) of both classical approaches and state of the art methods and brief description of each approach. ## further readings in this section we provide an overview of related tasks, which handle (explicit) anaphora in dialogue or implicit information in written text settings and may serve as a useful reference as they aim to address similar problems. anaphora. anaphora resolution is the task of identifying which parts of a text refer to the same discourse entity, which is based on the idea that different expressions can refer to the same entity. @xcite provide a survey of approaches to anaphora resolution in text. for dialog specific anaphora resolution, there are multiple shared tasks which have been organized, such as the codi-crac 2021 shared task on anaphora resolution in (spoken) dialogues @xcite , which focuses on entity coreference resolution, bridging resolution, discourse deixis/abstract phenomena as a follow-up of crac-18. additionally datasets such as mudoco @xcite provide annotations for thousands of dialogs, which contain entity mentions and coreference links. ellipsis. several studies have investigated the detection and resolution of ellipsis in written texts. for example, previous work applied classical machine learning techniques to detecting and resolving ellipsis in the british national corpus @xcite and in the penn treebank @xcite . earlier work approached ellipses with syntactic patterns @xcite . most work has focused on verb ellipsis, with the first study on noun ellipsis detection in texts performed by @xcite , who took a small dataset from the ud treebank that did not contain a noun phrase. for detection and resolution, they used a rule-based system using syntactic constraints of licensors of ellipsis and part-of-speech similarity between the licensors of ellipsis and the modifier of the antecedent. ## future directions after presenting the current state of research on implicit reference in dialog, we propose the following future directions: benchmarking resolving implicit references in dialog has primarily been explored through the tasks of conversational semantic role labeling or conversational utterance rewriting. in conversational utterance rewriting in particular, results are reported on different datasets in different languages and with various settings. thus, it is very challenging to draw conclusions and to compare among proposed computational methods. therefore, one of the first steps towards advancing systems for resolving implicit references in dialog is to establish a model agnostic benchmark, such as glue @xcite , to collect resources for training, evaluating, analysing such systems.