LED : A Dataset for Life Event Extraction from Dialogs (2023)

Abstract: Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conversations is rarely explored. In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. In addition, we initiate a novel Conversational Life Event Extraction task and differentiate the task from the public event extraction or the life event extraction from other sources like microblogs. We explore three information extraction (IE) frameworks to address the Conversational Life Event Extraction task: OpenIE, relation extraction, and event extraction. A comprehensive empirical analysis of the three baselines is established. The results suggest that the current event extraction model still struggles with extracting life events from human daily conversations. Our proposed Life Event Dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.

## introduction daily conversation, as a means of communication and switching information, is full of personal information, including personal background, interests and hobbies, connections to other people, and various life events. mining life events lets us better understand a person. the extracted life events can be used to construct the personal knowledge base and benefit a variety of downstream tasks, such as lifestyle understanding @xcite and memory assistance @xcite . previous research on life event extraction mainly focuses on life events from microblogs or social media platforms such as twitter @xcite @xcite . however, these events from a given fixed passage are static. in contrast, an event mentioned in a conversation might change its status dynamically throughout the chat. besides, conversations allow participants to interact with each other and gather the information which stimulates participants' interests, revealing people's general interests in different aspects of information about a life event and expanding additional event information. for example, when a person talks about a travel event only with the destination mentioned, the other interlocutor might ask additional information about who they are traveling with, how much the trip cost, and the period and timing of the travel. nevertheless, life event extraction from conversations is rarely explored and existing works only detect course or ambiguous event types @xcite . the participants and status of events are not recognized, preventing more fine-grained life events analysis and limiting the applications. we present life event dialog (led), a dataset with refined life event annotations in english. 1 we define life events as activities in a person's daily life. following previous works, our life event definition is verb-centered. for each event, we annotate three levels of event type from fine-grained to coarse: verb, class, and frame. unlike formal writing and social network posts, dialogue is usually in a more flexible and more abstruse style, where the event type is often omitted. for example, "s1: can i get you some coffee? s2: de-caff." indicates an "order" event, where the verb "order" does not appear in the dialogue. therefore, we also introduce explicitness of an event. when the event type cannot be extracted from the dialogue, we manually assign a verb to denote the activity and label the event as an implicit event. besides event types, we annotate subject and object of each event as event participants. furthermore, based on the interactive nature of a conversation, more detailed event information is likely to be revealed as the conversation continues. people might ask follow-up questions or clarifications in a response that specify the status or attributes of a known event. we consider the new supplemental information as the event status change instead of a new event. to be more specific, we record three aspects of event status: polarity, modality, and time. these detailed annotations provide more comprehensive information about life events and allow us to track the dynamic event status changes throughout the conversation. moving forward from previous research on classifying the types of life events, we introduce the conversational life event extraction task, which classifies the event type and identifies event participants simultaneously from conversations. classifying the event type of a life event is much harder than conventional public event extraction because of the high diversity of life events. the form of conversation further adds up to the difficulty of this task. for instance, event participants are challenging to identify because they are often in free form, and mentions of the same entity are easily changed throughout the dialogue. due to the uniqueness of conversational life event extraction, there has not been a model that specifically tackles this problem. in this paper, we examine multiple information extraction (ie) frameworks, including openie, event extraction (ee), and end-to-end relation extraction (re) models, for this task. experimental results show that the existing information extraction models, even the recent models on top of their tasks, still perform poorly in extracting life events from conversations. we analyze the strengths and limitations of each model, and urge the development of a better model for conversational life event extraction. the contributions of this work are threefold as follows: ## life event dialog in this paper, we define life events as daily life activities, personal habits, life experiences, or personal information of the interlocutors or related people. on the other hand, personal feelings or preference, public issues, and general knowledge are not considered life events in our dataset. ## conversational life event extraction we define conversational life event extraction as the combination of two subtasks: (1) event type classification and (2) participants identification. given a dialogue d @xmath0, t 2 , ..., t u } of u turns utterances, we extract i events e i @xmath1, ..., e i u } from d u , where an event e comprises an event type from either verb, class, or frame and spans of participants (s and o). we consider an input instance as the concatenation of turns t 1 to t u . ## conclusion this work presents life event dialog: a comprehensive life event dataset annotated on dailydialog conversations. the main differences between our dataset and previous datasets on personal life event extraction are: (1) life event dialog is built on top of conversations instead of microblogs like twitter. the interaction between speakers adds dynamics to events, such as information expansion or status modification, and indicates people's general interests in multiple aspects of other's life events. (2) life event dialog contains more data, more types, and more fine-grained event annotations compared to other conversational life event datasets. we propose the conversational life event extraction task, extending life event extraction tasks from social media to the conversation domain and from event type detection to predicting both event type and participants simultaneously. we then carefully examine three information extraction frameworks: openie, relation extraction (re), and event extraction (ee), for the pilot study on this task. the result suggests that current top models on three closely related fields cannot perform well in the conversational life event extraction task. improving object identification and implicit event extraction, detecting unseen life events, and keeping track of event status, constitute our future work.