Wizard of Wikipedia: Knowledge-Powered Conversational Agents

1 INTRODUCTION . Arguably , one of the key goals of AI , and the ultimate the goal of natural language research , is for humans to be able to talk to machines . In order to get close to this goal , machines must master a number of skills : to be able to comprehend language , employ memory to retain and recall knowledge , to reason about these concepts together , and finally output a response that both fulfills functional goals in the conversation while simultaneously being captivating to their human speaking partner . The current state-of-the-art approaches , sequence to sequence models of various kinds ( Sutskever et al. , 2014 ; Vinyals & Le , 2015 ; Serban et al. , 2016 ; Vaswani et al. , 2017 ) attempt to address some of these skills , but generally suffer from an inability to bring memory and knowledge to bear ; as indicated by their name , they involve encoding an input sequence , providing limited reasoning by transforming their hidden state given the input , and then decoding to an output . To converse intelligently on a given topic , a speaker clearly needs knowledge of that subject , and it is our contention here that more direct knowledge memory mechanisms need to be employed . In this work we consider setups where this can be naturally measured and built . We consider the task of open-domain dialogue , where two speakers conduct open-ended chit-chat given an initial starting topic , and during the course of the conversation the topic can broaden or focus on related themes . During such conversations , an interlocutor can glean new information and personal points of view from their speaking partner , while providing similarly themselves . This is a challenging task as it requires several components not found in many standard models . We design a set of architectures specifically for this goal that combine elements of Memory Network architectures ( Sukhbaatar et al. , 2015 ) to retrieve knowledge and read and condition on it , and Transformer architectures ( Vaswani et al. , 2017 ) to provide state-of-the-art text representations and sequence models for generating outputs , which we term Transformer Memory Networks . As , to our knowledge , no public domain dataset of requisite scale exists , we build a supervised dataset of human-human conversations using crowd-sourced workers , first crowd-sourcing 1365 diverse discussion topics and then conversations involving 201 , 999 utterances about them . Each ∗Joint first authors . topic is connected to Wikipedia , and one of the humans ( the wizard ) is asked to link the knowledge they use to sentences from existing articles . In this way , we have both a natural way to train a knowledgeable conversation agent , by employing a memory component that can recall and ground on this existing text , and a natural way to evaluate models that we build , by assessing their ability at locating and using such knowledge . Our Transformer Memory Network architectures , both in retrieval and generative versions , are tested in this setup using both automatic metrics and human evaluations . We show their ability to execute engaging knowledgeable conversations with humans , compared to a number of baselines such as standard Memory Networks or Transformers . Our new benchmark , publicly in ParlAI ( http : // parl.ai/projects/wizard of wikipedia/ ) , aims to encourage and measure further improvements in this important research direction . 2 RELATED WORK . Many existing dialogue tasks do not study the use of knowledge explicitly . For example , popular chit-chat datasets such as Open-Subtitles ( Vinyals & Le , 2015 ) , Persona-Chat ( Zhang et al. , 2018 ) and Twitter ( Sordoni et al. , 2015 ) have tested the ability of sequence-to-sequence models that attend over the recent dialogue history , but do not attempt to recall long-term knowledge beyond encoding it directly into the weights of the feed-forward network . In the area of goal-directed dialogue , separate from open domain chit-chat , such as airline ( El Asri et al. , 2017 ) or restaurant booking ( Henderson et al. , 2014 ; Wen et al. , 2016 ; Bordes et al. , 2017 ) , knowledge conditioning is typically employed by allowing access to a database through API calls or otherwise . In contrast , our work investigates unstructured knowledge across a large , diverse set of topics potentially spanning all of Wikipedia . In question answering one does not produce a dialogue response based on a conversation history , but a factual answer based on a question . In that case , it is clear that retrieving and conditioning knowledge is vital . For example , in SQuAD neural models have been developed that attend to a given paragraph from Wikipedia to answer questions ( Rajpurkar et al. , 2016 ) , or Open-SQuAD which extends this to answering without being given the paragraph , instead performing retrieval over the entirety of Wikipedia ( Chen et al. , 2017 ) . Recently , the QuAC dataset investigates similar themes , but as a sequence of questions and answers in dialogue form instead ( Choi et al. , 2018 ) . In this work we do not address question answering , but focus on natural human dialogues which contain a diverse set of utterances , not just questions and answers . The closest work to ours lies in the area of non-goal directed dialogue incorporating knowledge . The work of Dodge et al . ( 2016 ) employed Memory Networks to perform dialogue discussing movies in terms of recommendation and open-ended discussion from Reddit , conditioning on a structured knowledge base . Zhou et al . ( 2018 ) also links Reddit to structured knowledge . Both Parthasarathi & Pineau ( 2018 ) and Ghazvininejad et al . ( 2018 ) use unstructured text instead , as we do : the former to discuss news articles using Wikipedia summaries as knowledge , and the latter to discuss local businesses in two-turn dialogues using Foursquare tips as knowledge . Ghazvininejad et al . ( 2018 ) uses an extended Encoder-Decoder where the decoder is provided with an encoding of the context along with the external knowledge encoding . Neither involves dialogue authored with the given knowledge , so it is unclear when knowledge is useful or not . In contrast , in our task , we know the Wikipedia articles and sentences that ground crowdworkers dialogues . Model-wise , Parthasarathi & Pineau ( 2018 ) uses a Bag-of-Words Memory Network type fact encoder and an RNN decoder . Our work compares Memory Networks ( Sukhbaatar et al. , 2015 ) and Transformers which have been shown to be on-par or superior to RNN encoder-decoders ( Vaswani et al. , 2017 ) , and develops an architecture that combines these approaches . Concurrently with our work Moghe et al . ( 2018 ) proposed a dataset based on the closed domain of movie chats . Our paper shows models working on full multi-turn dialogue in an open-domain setting , which to our knowledge was not shown before . 3 WIZARD OF WIKIPEDIA . We consider the following general open-domain dialogue setting : two participants engage in chitchat , with one of the participants selecting a beginning topic , and during the conversation the topic is allowed to naturally change . The two participants , however , are not quite symmetric : one will play the role of a knowledgeable expert ( which we refer to as the wizard ) while the other is a curious learner ( the apprentice ) . Apprentice At each stage of the conversation the apprentice talks to the wizard freely , playing the role of a curious learner , eager to chat . Their goal is to go into depth about a chosen topic that interests themselves or their partner , while keeping the conversation engaging and fun . Note that the instruction to delve deeply into a topic makes this different to more “ shallow ” chit-chat tasks . In this task the use of knowledge is emphasized more . Wizard The wizard is given the following instructions : “ You have just met the other person , who seems quite curious , and you are eager to discuss a topic with them ! ” Their goal is to inform their conversation partner about a topic that one of them will choose . Crucially , the wizard has access to an information retrieval system that shows them paragraphs from Wikipedia possibly relevant to the conversation , which are unobserved by the apprentice . Before each conversation turn the wizard can read these paragraphs and then potentially base their next reply on that observed knowledge . Note , the wizard is particularly instructed not to simply parrot this knowledge , but to use it to craft a relevant reply , and to present any relevant knowledge in a fun and engaging way , if possible . Conversation Flow The flow of the conversation thus takes place as follows . 1 . Either the wizard or apprentice is picked to choose the topic and speak first . The other player receives the topic information , and the conversation begins . 2 . When the apprentice sends the wizard a message , the wizard is shown relevant knowledge ( described below ) , and chooses a relevant sentence in order to construct a response , or else chooses the no sentence used option . 3 . The Wizard responds to the apprentice basing their response on their chosen sentence . 4 . The conversation repeats until one of the conversation partners ends the chat ( after a minimum of 4 or 5 turns each , randomly chosen beforehand ) . After collecting data of such wizard-apprentice conversations between humans , the goal is to then replace the human wizard with a learned agent that will speak to a human apprentice instead , similar to the procedure in Wizard of Oz experiments ( Bernsen et al. , 2012 ) . Topics We crowd-sourced a set of 1365 natural , open-domain dialogue topics , each linked to a Wikipedia article . These include diverse topics such as commuting , Gouda cheese , music festivals , podcasts , bowling , and Arnold Schwarzenegger . Knowledge Retrieval At each step of the dialogue the wizard has access to a set of passages of knowledge which may be relevant to the given dialogue context . While this is a potentially learnable part of the model , we required for this to be fixed so that we could present the results to the annotator when collecting the dataset . We thus used exactly the same retriever that is commonly used for the Open-SQuAD dataset in Chen et al . ( 2017 ) . It uses a simple inverted index lookup followed by term vector model scoring . Articles and queries are compared as TF-IDF weighted bag-of-word and n-gram vectors , using the hashing trick . We retrieve the top 7 articles ( first paragraph only ) for the last two turns of dialogue ( by wizard and apprentice ) and the article ( first 10 sentences only ) for the original topic , and present these articles to the wizard as knowledge context , along with their titles . Note that while this system is used to build the dataset , a superior method can in principle be learned and used by a model at test time . Knowledge Selection and Response Generation During data collection , the wizard can click on any of the retrieved article titles in the dialogue UI to expand that article , at which point they can click on a sentence that is most relevant to the response they want to make ( only one article , and one sentence can be selected on any turn , for simplicity ) . If they see no relevant article or sentence they can choose no sentence used instead . The wizard then enters their response to the apprentice . An image of the Wizard ’ s UI is shown in Appendix A.1 . Final Dialogue Dataset The final dialogue dataset we collect consists of 22,311 dialogues with 201,999 turns , which we divide into 166,787 for train , 17,715 for validation , and 17,497 for test . The test set is split into two subsets , Test Seen and Test Unseen . Test Seen contains 533 overlapping topics with the training set with new dialogues about those topics . Test Unseen consists of 58 topics never seen before in train or validation . Overall data statistics can be found in Table 1 , and further statistics and examples of collected conversations in Appendix A.2 . We observe wizards and apprentices both asking and answering questions , and providing each other with a mixture of facts and personal feelings during their general discussion .

Summary: This work proposes a brand new dataset to fill in the vacancy of current conversational AI community, specifically the introduced dataset aims at providing a platform to perform large-scaled knowledge-grounded chit-chat. Overall, the dataset is well-motivated and well-designed, its existence will potentially benefit the community and inspire more effective methods to leverage external knowledge into dialog system. Besides, the paper also utilizes many trending models like Transformers, Memory Networks, etc to ensure the state-of-the-art performance. The clear structure and paragraphs also makes the paper easy to read and follow.