Extracting and Inferring Personal Attributes from Dialogue (2022)

Abstract: Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialogue, and analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation. Finally, we demonstrate the benefit of incorporating personal attributes in social chit-chat and task-oriented dialogue settings.

## introduction personal attributes are structured information about a person, such as what they like, what they have, and what their favorite things are. these attributes are commonly revealed either explicitly or implicitly during social dialogue as shown in figure 1 , allowing people to know more about one another. these personal attributes, represented in the form of knowledge graph triples (e.g. i, has_hobby, volunteer), can represent large numbers of personal attributes in an interpretable manner, facilitating their usage by weakly-coupled downstream dialogue tasks @xcite @xcite . one such task is to ground open-domain chitchat dialogue agents to minimize inconsistencies in their language use (e.g., i like @xmath0, 2020). thus far, personalization in chit-chat has made use of dense embeddings and natural language sentences. while kg triples have been shown to be capable of grounding natural language generation @xcite , they have yet to be used to personalize chit-chat dialogue agents. personal attributes can also help task-oriented dialogue agents to provide personalized recommendations @xcite @xcite @xcite . such personalized recommendations have only been attempted for single-domain tasks with a small set of one-hot features (< 30). personalization across a wide range of tasks (recommending food, movies and music by multi-task dialogue agents such as alexa, siri and assistant) however can require orders of magnitude more personal attribute features. this makes kg triples ideal for representing them, given the advantages of this data structure for models to select and utilize pertinent features @xcite . based on these advantages, we investigate how personal attributes can be predicted from dialogue. an important bottleneck for this step lies in the poor coverage of relevant personal attributes in existing labeled datasets. therefore, we introduce two new tasks for identifying personal attributes in section 2. as shown in figure 1 , the extraction task requires determining which phrase in an utterance indicate a personal attribute, while the inference task adds further challenge by requiring models to predict personal attributes that are not explicitly stated verbatim in utterances. this is common in conversational settings, where people express personal attributes using a variety of semantically related words or imply them using commonsense reasoning. we analyze how these factors allow personal attributes to be linked to utterances that express them. to tackle these tasks, we propose a simple yet extensible model, genre, in section 3. genre combines a constrained attribute generation model (that is flexible to accommodate attributes not found verbatim in utterances) with a discriminative reranker (that can contrast between highly similar candidates). our experiments in section 4 suggest that such design allows our model to outperform strong baseline models on both the extraction and inference tasks. subsequently in section 5, detailed ablation studies demonstrate the value of our model components while further analysis identifies future areas for improvement. finally in section 6, we show how personal attributes in the form of kg triples can improve the personalization of open-domain social chit-chat agents as well as task-oriented dialogue agents. in the former case, personal attributes can be utilized to improve chat-bot consistency on the per-sonachat task @xcite . in the latter case, we suggest how our personal attributes can support personalization in multi-task, task-oriented dialogue settings. ## personal attribute tasks based on the usefulness of personal attributes for dialogue personalization, we propose the task of obtaining personal attributes from natural language sentences. we first explain how we formulate two complementary tasks from dialognli data and then formally define our tasks. finally, we analyze the task datasets to gather insights into the linguistic phenomena that our tasks involve. ## genre this section proposes genre, a model that uses a unified architecture for both the extraction and the inference tasks. we use a simple and extensible generator-reranker framework to address the needs of the two tasks. on one hand, a generative model is necessary because head and/or tail entities cannot be directly extracted from the sentence for the inference dataset. on the other hand, preliminary experiments using a generator in isolation showed that a large proportion of correct triples are among the top-k -but not top-1 -outputs. a reranker can be used to select the most likely triple among the top-k candidate triples, leading to a large improvement in performance (see ## experiments we first explain the metrics used in the experiments. next, we introduce the baseline models. finally, we examine how genre compares to baseline models to understand its advantages. ## analysis we first conduct an ablation study to better understand the contribution of constrained generation and the reranker, by measuring the performance of our model when each component is removed. then, we seek to understand how errors are made on predicted personal attribute relations to identify future areas of improvement. ## applications of personal attributes personal attributes can make social chit-chat agents more consistent and engaging as well as enable task-oriented agents to make personalized recommendations. in this section, we use personal at- top 3 most frequent (n) dataset true relation (n) p r f1 predicted relations true tail entities predicted tail entities extraction [has_profession] (274) 83.8 62.0 71.3 [has_profession] (189) teacher (29) nurse (27) [employed_by_general] (30) nurse (28) real estate (25) [want_job] (17) real estate agent (25) teacher (19) [have_pet] (149) 97.3 55.0 70.3 [have_pet] (88) dog (55) cat (32) [have_family] (18) cat (45) pets (23) [like_animal] (12) pets (22) dog (18) inference [like_food] (77) 46.7 41.6 44.0 [like_food] (62) pizza (18) pizza (19) [like_activity] (5) onion (9) italian cuisine (10) [like_animal] (4) italian (7) onion (8) [like_music] (71) 40.8 23.9 30.2 [like_music] (40) jazz (10) the story so far (12) [favorite_music_artist] (9) country (9) country (8) [like_activity] (7) rap (6) jazz (7) ## conclusion in conclusion, we propose the novel tasks of extracting and inferring personal attributes from dialogue and carefully analyze the linguistic demands of these tasks. to meet the challenges of our tasks, we present genre, a model which combines constrained attribute generation and re-ranking on top of pre-trained language models. genre achieves the best performance vs. established relation extraction baselines on the extraction task (≥ 3.7 f1 points) as well as the more challenging infer-ence task that involves lexical and commonsense inferences (≥ 5.1 f1 points). together, our work contributes an important step towards realizing the potential of personal attributes in personalization of social chit-chat and task-oriented dialogue agents.