I Know the Feeling: Learning to Converse with Empathy

1 INTRODUCTION . Natural communication is frequently prompted by people sharing their feelings or circumstances . As examples , a recent study found that 80 % of Twitter users seem to post mostly about themselves ( Naaman et al. , 2010 ) , and ELIZA ( Weizenbaum , 1966 ) , one of the earliest chatbots developed , focused on asking its conversational partners why they were feeling a certain way . Interacting in these conversations requires reacting to what people share with an understanding of others ’ implied feelings . For instance , while the crossed-out response in Figure 1 is topically relevant , “ Congrats ! That ’ s great ! ” is more natural because it acknowledges the underlying feelings of accomplishment . Responding to people in a way that is empathetic or that acknowledges how the other person feels is a desirable trait for a dialogue agent , but still a nontrivial communicative skill . It is also currently difficult to measure . Although recent work has used large-scale corpora to train reasonably fluent and engaging dialogue agents ( e.g . Mazaré et al . ( 2018 ) ) , existing chitchat dialogue benchmarks are not designed to capture whether those agents are responding in an empathetic way to implicit emotional cues . This may indeed be unlikely , given that the sources of Internet conversation data used for training are known to harbor aggressive and callous responses ( Anderson , 2015 ) . This works aims to make it easier to evaluate machines ’ ability to respond in an empathetic way . We introduce a new task for dialogue systems to respond to people discussing everyday situations , based on EMPATHETICDIALOGUES , a novel dataset with 25k personal dialogues . Each dialogue is grounded in a specific situation where a speaker was feeling a given emotion , with a listener responding . The dataset is larger and contains a more extensive set of emotions than many similar emotion prediction datasets from other text domains such as Scherer & Wallbott ( 1994 ) , Strapparava & Mihalcea ( 2007 ) , Mohammad et al . ( 2018 ) , and Gupta et al . ( 2017 ) . Previous dialogue datasets of a similar scale that include emotion labels ( Li et al. , 2017 ; Gupta et al. , 2017 ) come from crawled conversations extracted from settings that are quite different from a one-on-one conversation ( educational dialogues for English learners for DAILYDIALOG , public social media content for Gupta et al . ( 2017 ) ) and cover either a very limited or a very imbalanced set of emotions : only≈ 5 % of the DailyDialog utterances have a label other than ‘ none ’ or ‘ happy ’ , and Gupta et al . ( 2017 ) only labels ‘ happy ’ , ‘ sad ’ , and ‘ angry ’ . The open resource we propose consists of crowdsourced one-on-one conversations , and covers a large set of emotions in a balanced way . We then examine how to train a dialogue system that is more adept at responding to emotional cues . While a rule-based system can be built around mapping predicted emotions to responses , end-toend dialogue systems relying on neural networks and trained on conversation corpora ( Shang et al. , 2015 ; Vinyals & Le , 2015 ; Sordoni et al. , 2015 ; Serban et al. , 2015 ; Dodge et al. , 2016 ; Mazaré et al. , 2018 ; Zhang et al. , 2018 ) offer the promise of better generalization to new contexts . Through an extensive set of experiments , we show that fine-tuning a dialogue agent on our dataset results in better performance on a novel empathetic dialogue task . The pretraining of the dialogue agent on Internet conversation data is the most time-consuming step of this pipeline , by an order of magnitude . To make it easier for practitioners to improve performance of a model on the empathetic task with minimal re-training while re-using existing resources , we compare various ways of supplementing a pretrained model with additional representations from external tasks , and show that even simplistic schemes can lead to better performance . The contributions of this work are thus threefold : 1 ) we release a novel empathetic dialogue dataset as a new benchmark ; 2 ) we show that using this dataset for training can improve the performance of an end-to-end dialogue system on empathetic dialogue ; and 3 ) we compare multiple ways to further improve performance with combined representations while not requiring onerous re-training . 2 RELATED WORK . Responding well to emotions requires sufficient coverage of human expression . Multiple schemas have attempted to organize the spectrum of emotions , from a handful of basic emotions derived from biological responses ( Ekman , 1992 ; Plutchik , 1984 ) to larger sets of subtle emotions inferred from contextual situations ( Skerry & Saxe , 2015 ) . We incorporate emotions from multiple annotation schemas , noting that emotions merely inferred from a situation are important in dialogue scenarios . Rich information can be represented by learning multidimensional distributional embeddings from data , as has proven successful for many language applications ( Grave et al. , 2018 ) . These distributional representation approaches are at the core of the current state of the art in emotion classification ( Duppada et al. , 2018 ; Park et al. , 2018 ; Xu et al. , 2018 ; Mohammad et al. , 2018 ) that build on deep networks pretrained on large-scale weakly labelled data such as emojis ( Felbo et al. , 2017 ) or hashtags ( Mohammad , 2012 ) , gathered from public social media content published on Twitter . The SEMEVAL2019 EmoContext challenge also uses conversation data for detection of three basic Under review as a conference paper at ICLR 2019 % 0 % 25 % 50 % 75 % 100 % surprised impressed joyful content excited afraid prepared devastated annoyed disgusted guilty sentimental proud confident furious caring angry terrified nostalgic trusting sad hopeful jealous ashamed grateful anxious anticipating apprehensive lonely disappointed embarrassed faithful emotions over two turns of context from Twitter exchanges ( Gupta et al. , 2017 ) . While public social media content has the advantage of being spontaneous ( not elicited ) data , it suffers from two shortcomings when used to train a model intended for one-on-one conversation ( as opposed to , say , a bot designed to post on Twitter ) . First , the content is extracted from a context of communication in front of large ” peripheral audiences ” ( Goffman , 1981 ) which include potentially everyone with an Internet connection , where the need for curated self-presentation ( Goffman , 1959 ) and the uncertainty as to how wide that audience may be have been shown to lead to different choices of subject matters compared to private messaging , with people sharing more intense and negative emotions through private channels ( Bazarova et al. , 2015 ; Litt et al. , 2014 ) . Second , Tweets are generally a short-form format limited to 140 characters , which is not a constraint that applies to general conversation . In this work , we attempt to generate a more balanced coverage of emotions than would appear in public social media content , within a one-on-one framing of unconstrained utterances that is closer to our ultimate goal of training a model for conversation that can respond to any emotion . Several works have attempted to make chit-chat dialogue models more engaging by grounding them in personal contexts ( Li et al. , 2016 ; Zhang et al. , 2018 ; Mazaré et al. , 2018 ) , but focusing on personal facts ( “ I am from New York ” ) rather than situations . The DAILYDIALOG dataset ( Li et al. , 2017 ) , comprising about 13k dialogues obtained by crawling educational websites intended for learners of English , includes many dialogues anchored in everyday situations and has been annotated post-hoc with emotion labels , but only ≈ 5 % of the utterances have a label other than “ none ” or “ happy ” , and dialogues are mostly limited to domains deemed appropriate for use as a language learning tool ( ordering from a restaurant , asking for directions , shopping for a specific item , introductions , etc ) . Our task focuses explicitly on conversations about an emotionally grounded situation , and considers a richer , evenly distributed set of emotions . We also introduce an explicit single listener in the conversation who is reacting to the situation being described in an empathetic way , to make the setting as close as possible to our desired goal of a one-on-one empathetic conversation . Several other works have focused on controlling the emotional content of a text response either through a manually specified target ( Zhou & Wang , 2018 ; Zhou et al. , 2017 ; Wang & Wan , 2018 ; Hu et al. , 2017 ; Huang et al. , 2018 ) or through a general term to encourage higher levels of affect ( Asghar et al. , 2018 ) , with evaluations focused on matching a predetermined desired emotion rather than empathetic responding . Niu & Bansal ( 2018 ) generate responses conditioned on a specified politeness setting ( polite , rude or neutral ) , where politeness is viewed as a style of language . Huber et al . ( 2018 ) investigate how to respond to emotions detected from an image . By contrast , our work examines how to produce empathetic responses that are appropriate to signals inferred purely from text , and not intended to themselves convey a pre-specified emotion . 3 TALKING ABOUT PERSONAL SITUATIONS . We consider an open-domain one-on-one conversational setting where two people are discussing a situation that happened to one of them and that led to the experience of a given feeling . Emotional situation grounding Each conversation is grounded in a situation , which one participant writes about in association with a given emotion label . We consider 32 emotion labels , listed in Figure 2 . To select this set of labels , we drew inspiration from previous datasets ( Scherer & Wall- bott , 1994 ; Strapparava & Mihalcea , 2007 ; Skerry & Saxe , 2015 ; Li et al. , 2017 ; Mohammad , 2012 ) , consolidating the labels from each into a merged list . Speaker and Listener The person who wrote the situation description ( Speaker ) initiates a conversation to talk about it . The other conversation participant ( Listener ) becomes aware of the underlying situation through what the Speaker says and responds . Speaker and Listener then exchange up to 6 more turns . We include two example conversations from the training data in Table 1 . The models discussed below are tested in the role of Listener responding to the Speaker . The situation description generated by the Speaker is not given to the models ( just as it was not given to the Listener during dialogue collection ) . Our data could also be used to generate conversations for the Speaker conditioned on the situation description ; we leave this for later work . EMPATHETICDIALOGUES dataset statistics The resulting dataset comprises 24,850 conversations about a situation description , gathered from 810 different participants , which will be made publicly available online and through ParlAI . The crowdsourced dialogs were obtained using the ParlAI framework to interact with the Amazon Mechanical Turk ( MTurk ) platform . Details of the crowdsourcing procedure are given in the Supplemental Material . The distribution of emotion label prompts ( Table 2 ) is close to evenly distributed across categories with a few categories that are selected slightly more/less often . The average situation description is 19.8 words . Each conversation is allowed to be 4-8 utterances long ( the average is 4.3 utterances per conversation ) . The average utterance length is 15.2 words long . We split the conversations into approximately 80 % train , 10 % validation , and 10 % test partitions . To prevent overlap of discussed situations by a same participant between partitions in case a participant chose to discuss the same situation twice , we split the data so that all sets of conversations with the same speaker providing the initial situation description would be in the same partition . The final train/val/test split was 19533 / 2770 / 2547 conversations , respectively .

Summary: The overall goal of the paper is to make end-to-end dialogue systems more empathetic, so that they can respond more appropriately and in ways that acknowledge how the users are feeling. The authors make two contributions towards that goal: (1) they introduce a crowdsourced dataset (EmpatheticDialogue) annotated with fine-grained emotion labels. (2) They show improvements on dialogue generation (in terms of empathy, but also relevance and fluency) using a multi-task objective, ensemble of encoders, and a more ad-hoc technique that consists of prepending inferred emotion labels to the input.