PRIDE : P redicting R elationships in C onversations (2021)

Abstract: Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.

## introduction motivation and problem. personal knowledge about individual users is a valuable asset for personalizing downstream applications, such as intelligent assistants, recommender systems and search engines. however, such personalized services are commonly achieved with end-to-end learning approaches, where user information is bound to be in latent representation and inaccessible to users. explicit personal knowledge bases (pkbs) @xcite , which are built independently of any downstream application, serve as background knowledge for personalization. pkbs are crucial for empowering users with control over what can be learned from their data collected by big tech companies. such pkbs will also provide transparency and explainability to end users about inferred personal knowledge and any personalized decisions made by the systems. with the ubiquity of social media and online forums, user-generated content is available in abundance. mining personal knowledge from usergenerated content to populate pkbs, or user profiling, is a long-standing topic in nlp (e.g., @xcite @xcite . while users' demographic attributes and interests can be learned from their profile descriptions and posts, interpersonal relationships with other users are rarely mentioned explicitly and may only be inferred from their interactions and conversations. in this work, we develop an automatic method for predicting fine-grained relationships between two speakers, given their logged conversation history. consider the example in figure 1 . from the excerpt of interactions between a and b, the reader can figure out that b is the child of a by observing (i) the address term 'sweetheart', (ii) the commanding but soft tone of user a, (iii) the reference to the other family member 'dad @xcite the context created by the word 'homework'. yet, neither of the speakers directly mentions their relationship, making this task difficult for automatic methods relying on explicit patterns. the relationship information extracted from such conversations, e.g., b, child_of, a , can be entered into the pkbs of users a and b. by combining such relationship information with user b's age and personal interests (e.g., playing guitar, justin bieber) inferable from user b's social media (exemplified in figure 1 ), a system will be able to provide user a with relevant personalized recommendations for a query "birthday present ideas for my daughter". ## methodology the neural model architecture, inspired by @xcite , is shown in figure 2 . pride hierarchically creates word and utterance representations, which are then combined with representations of personal attributes and interpersonal dimensions ( ## dataset we present fire-a film relationship dataset, consisting of labeled relationships of fictional characters in popular movies, obtained via crowdsourcing. fire is based on movie scripts, which are a good approximation for real-life conversations. to the best of our knowledge, this is the first and the largest conversational dataset with directed, multilabel relationship labels. data preparation. we use the jinni movie dataset collected in gorinski and lapata (2018), which provides speaker labels for each utterance as well as the film genre metadata. we selected the movies which: for each pair of characters we kept only the film scenes where they are the only participants. additionally, we include all uninterrupted dialogue spans of the considered pair in the 3-character scenes (details are in appendix a.2). we kept only the pairs which have at least 30 utterances throughout the whole movie. ## experimental setup data splitting and preprocessing. we performed five-fold cross-validation, where the folds are arranged so that the sets of movies, where the input character pairs come from, are disjoint. we additionally balanced label distributions as described in appendix c.1. we trained the models on three folds and chose hyperparameter settings according to the performance on 1-fold validation set. we report the results on the remaining 1-fold test set. from the input scripts we removed personal names foot_1 and movie-specific words (which we defined as words found in only one movie script), to reduce overfitting to movie domain or genre. ## conclusion we presented pride, a model for predicting finegrained relationships from conversations. our results illustrate the utility of our approach, showing that pride outperforms state-of-the-art baselines and can effectively transfer learn on different types of dialogue data. in ablation experiments we demonstrated that the design decisions behind the model improve the quality of relationship prediction in conversations. to support future work on this topic, we created and released the largest labeled collection of relationships in conversations, which additionally improves over existing datasets by including asymmetric relationships.