CHARM: Inferring Personal Attributes from Conversations

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 5391–5404, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 5391 CHARM: Inferring Personal Attributes from Conversations Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum Max Planck Institute for Informatics Saarbr¨ucken, Germany {tigunova, ayates, paramita, weikum}@mpi-inf.mpg.de Abstract Personal knowledge about users’ professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individu- alized AI, such as recommenders or chatbots. Conversations in social media, such as Reddit, are a rich source of data for inferring personal facts. Prior work developed supervised meth- ods to extract this knowledge, but these ap- proaches can not generalize beyond attribute values with ample labeled training samples. This paper overcomes this limitation by devis- ing CHARM: a zero-shot learning method that creatively leverages keyword extraction and document retrieval in order to predict attribute values that were never seen during training. Experiments with large datasets from Reddit show the viability of CHARM for open-ended attributes, such as professions and hobbies. 1 Introduction Motivation. Personal Knowledge Bases (PKBs) capture individual user traits for customizing down- stream applications like chatbots or recommender systems (Balog et al., 2019). A potentially au- tomatic way to populate a PKB is to draw per- sonal knowledge from the user’s conversations in social media and dialogues on other platforms. These interactions are a rich source of personal attributes, such as hobbies, professions, cities vis- ited, medical conditions (experienced by the user) and many more. Each of these would consist of key-value pairs, such as cities visited:Paris or symp- tom:dizziness. However, a large number of poten- tial attributes and their respective values makes this a challenging task. In particular, there is little hope to have training data for each of these key-value pairs. Moreover, the textual cues in user conversa- tions are often implicit and thus difficult to learn. Example. Consider the user’s utterance: “I just visited London, which was a disaster. My hotel was a headache and I spent half the time in bed with a fever... So glad to be back home finishing the masts on my galleon.” As humans, we can infer the following attribute-value pairs: (a) cities vis- ited:London, (b) symptom:fever, (c) hobby:model ships. Capturing such user traits is a daunting task, however, with both implicit and explicit signals present. We need to consider the context “spent in bed with”, to infer that fever relates to a disease (as opposed to headache). To predict the user’s hobby model ships, we have to pay attention to the cues ‘galleon’ and ‘mast’. Proper inference re- quires both deep language understanding and back- ground knowledge (e.g., about ships, cities, etc.). State of the Art and its Limitations. Explicit mentions of attribute-value pairs can be captured by pattern-based methods (e.g., Li et al. (2014); Yen et al. (2019)). Such methods are able to extract London from the the previous example by using the pattern “I ...visited ⟨city name⟩”. Pattern-based approaches are limited, though, by their inability to consider implicit …

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