A Survey of Ontology Expansion for Conversational Understanding (2024)

Abstract: In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges. This survey aspires to be a foundational reference for researchers and practitioners, promoting further exploration and innovation in this crucial domain.

## introduction conversational understanding (cu) is a core component in the development of conversational agents @xcite . the objective of the cu module is to accurately capture and interpret user needs during interactions. as illustrated in figure 1 , these capabilities are generally encapsulated within a conversational ontology, which defines a collection of possible user intents, slots, and values for each slot @xcite @xcite . effective cu models must not only identify the overall purposes (intent detection) @xcite expressed by users but also pinpoint relevant pieces of information (slot filling) @xcite that fulfill these intents. traditionally, cu research assumes a welldefined, static ontology where all intents, slots, and most possible values are predetermined. within book inform area price day intents slots values check vaccination status vaccine brands can you tell me if there are any pfizer or moderna vaccines available right now? intent: check vaccination status slot: vaccine brands value1: pfizer value2: moderna predefined ontology new ontological items moderna pfizer utterance understanding update ontology center expensive cheap june 1st this predefined framework, cu is often treated as a closed-world classification task for intents and sequence labeling task for slot values @xcite . however, in real-world settings, conversational agents encounter rapidly evolving user needs and diverse expressions, leading to the emergence of new ontological items @xcite . this dynamic environment presents a significant challenge, as traditional cu models fail easily in situations beyond the predefined ontology. to address this challenge, onexp has been proposed to facilitate open-world ontology learning @xcite @xcite . it dynamically updates and extends the conversational ontology by recognizing both pre-established and novel ontological items from user utterances. effective onexp approaches can significantly enhance the downstream decisionmaking and policy implementation of conversational agents, improving user satisfaction and service efficiency @xcite . recent years have witnessed substantial progress in developing innovative onexp methodologies. however, the rapid advancements have left a gap in comprehensive reviews that summarize these efforts and discuss emerging trends. this paper aims to fill this gap by providing a thorough survey of onexp research. we introduce the preliminaries of dataset domain #samples #ontologies supported tasks #intents #slots nid nsvd banking77 @xcite ) bank 13,083 77 -✓ ✗ clinc150 @xcite multi-domain 22,500 150 -✓ ✗ stackoverflow @xcite question 20,000 20 -✓ ✗ ## taxonomy of onexp research this section presents the new taxonomy for on-exp as shown in figure 2 , comprising new intent discovery ( §3.1), new slot-value discovery ( §3.2), and joint onexp ( §3.3). ## conclusion and future directions this paper presents the first comprehensive survey of recent advances in onexp. we begin by formulating the task, detailing representative data resources and evaluation protocols used. we then examine prevalent onexp methods, including nid, nsvd, and joint onexp. despite significant progress achieved, several challenges remain, inspiring promising frontiers for future research. early onexp. existing studies primarily concentrate on developing models to expand predefined ontologies using extensive utterances. yet, realworld conversational agents necessitate the ability to rapidly recognize and adapt to evolving user needs and dialogue contexts @xcite , thus highlighting the critical importance of earlystage onexp. early onexp faces the unique challenge of identifying new ontological items with minimal utterances when a known ontology has been established using extensive data. in such a scenario, nascent ontological items risk being submerged by more prevalent ones. although @xcite showcased the effectiveness of csepl in early intent discovery, more specific methods that fully address the unique challenges of this area remain largely under-explored. this highlights its significant potential as a promising field for future research. multi-modal onexp. current onexp tasks generally learned new ontological items from purely text-modal utterances. however, practical interactions with conversational agents typically occur in multi-modal settings @xcite @xcite , suggesting that such multimodal data can enhance new ontology learning. for example, incorporating visual data in e-commerce or audio cues in customer support could provide deeper contextual insights than text-only systems @xcite . despite its potential, multimodal onexp is still in its early stages, with limited research on effectively synergizing different modalities to expand ontologies. this emerging area promises to significantly improve the capabilities of conversational agents across different applications, necessitating more comprehensive research into advanced modality integration techniques and benchmarks of multi-modal data in onexp.