The Alexa Prize funded 12 international teams to compete to create a conversational agent that can discuss any topic for at least 20 minutes. UCSC's Slugbot was one of these funded teams. The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation. SlugBot's conversations over the semi-finals user evaluation averaged 8:17 minutes.Unlike much previous work on conversational AI, SlugBot could not and did not assume that the user had an “information need” BIBREF0 , BIBREF1 , BIBREF2 . Rather, the design of the Alexa Prize was aimed at open conversations that could engage the user, through any type of dialogue or chitchat, discussing films and books, gossiping about celebrities, playing verbal games, telling stories or sharing experiences, or any other of many different types of activities that conversation is often used for.This open design foregrounds many longstanding challenges that have not been solved even for task-oriented dialogue systems. These include:This paper is structured around the “lessons learned” with respect to these challenges from our experience building SlugBot. To be clear, we are not offering a solution to these problems: instead our intention is simply to highlight the difficulties with developing adequate computational models of these phenomena that particularly arise in the context of open-domain conversations, where users cannot be assumed to be pursuing a particular task or information need. We will attempt to motivate our hypothesis that a comprehensive solution to these challenges for open-domain dialogue requires a much deeper understanding and utilization of the semantic relations that underly dialogue coherence.For example, consider dialogue focused on content related to the movie domain. This should be one of the easiest domains because it is well-structured, and there are existing systems handling conversations where there is a specified user information need or task, such as finding films with particular properties, finding out what is playing and where, or booking a movie ticket BIBREF3 , BIBREF4 , BIBREF5 . Moreover, the Internet Movie Database (IMDB) BIBREF6 provides information on plot, rating, and actors that can be leveraged to support conversations. IMDB also makes use of the Schema.org BIBREF7 structure to connect common entities to their related attribute types (such as Actor $\rightarrow $ Person $\rightarrow $ birthDate), allowing the system to retrieve a large set of possible next topics and related facts and entities.However, remember that SlugBot is based on the assumption that the user might simply enjoy talking about films and related entities and therefore may freely move the conversational focus among different movie entities, along with the vast array of semantically-associated movie attributes: movies have actors, genres, plots, and awards; actors have names, affiliations, other movies they were in, awards, etc. Actors are people, who have spouses, families and friends, and engage in other life activities besides acting, such as political advocacy.A potential dialogue is shown in Table 1 . The interaction might appear to be simple enough: the user chooses to discuss movies, and selects Jason Bourne as the specific movie she is interested in, the system finds the movie in IMDB, and then provides information on its rating, lead actor, and plot. The user then changes the topic to other movies with the same actor, and the conversation continues.Even with the availability of IMDB, however, the interaction is not totally straightforward. The RHS of Table 1 describes some of the required competencies and decisions SlugBot must make. First, Slugbot must be able to perform coreference resolution and recognize that the movie and it in turns U6 and U8 are coreferential. We estimate the accuracy of noun-phrase coreference resolution to only be about 70% for off-the-shelf tools applied to dialogue, since most of them are targeted to text BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 .More challenging is that at each system turn, there are a large number of conversational moves that are possible. Making good decisions about what to say next requires balancing a dialogue policy as to what dialogue acts might be good in this context, with real-time information as to what types of content might be possible to use in this context. Slugbot could offer an opinion as in turn S3, ask a follow-on question as in S3, take the initiative to provide unasked for information, as in S5, or decide, e.g. in the case of the user's request for plot information, to use search to retrieve some relevant content. Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary. Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.In open-domain conversation, dialogue coherence between related turns must be maintained. What underlies dialogue coherence goes beyond simple word overlap or similarity, and its clear that neural models of open-domain conversational dialogue do not yet capture it. Theories of discourse posit that there are a small number of semantic relations that can hold between adjacent turns: at the most general level these are contingency, comparison, expansion, and temporal order BIBREF16 , BIBREF17 , BIBREF18 . We posit that one way to allow SlugBot to take the initiative and produce a turn that maintains discourse coherence is to find content to use in Slugbot's next turn that instantiates a valid semantic relation between the current user turn and SlugBot's next turn. One of the strongest bases for such semantic relations are the relations captured by ontologies or frames, which give us related entities, e.g. movies have actors and directors BIBREF4 , BIBREF21 . These types of relations can be used to instantiate the expansion relation, which basically captures moving to strongly related subtopics, often by chaining off a particular discourse entity. To find content to instantiate the expansion relation to use in Slugbot's next turn (taking the initiative), we carry out the following pipeline:In the case of movies, the structure of IMDB, as discussed above, allows us to link between related entities and attributes using various DB keys. However other conversational domains do not have freely available richly structured information such as this. It is rare for a single resource to aggregate all the information that might be useful, so SlugBot must be able to leverage information and integrate information from multiple sources. But state-of-the-art knowledge bases and ontologies are still limited. Table 2 lists some of the resources that we have found to be most useful for search and structured information.Like movies, sports is another domain that has rich structure, and in which there is broad user interest. Search results for a query about "Madison Bumgarner" are in Figure 1 , showcasing a sample of the different information retrievable from each source (Step 2 of the pipeline).From the Google Knowledge Graph (Figure 1 result we are able to ascertain the entity type, a brief description, and a relevant Wikipedia page (Figure 1 ) which we can use to find accurate structured information. We may further augment our knowledge by using the information returned by the Google Knowledge Graph as parameters to our YAGO or DBpedia query which can more easily extract specific relationships between an entity-attribute. For example, the results returned by YAGO for the "Madison Bumgarner" query contains a connection to the headline Struggling MadBum might not garner next start, which is contextually relevant data not encapsulated anywhere in the previously examined results.There, however, exists a disconnect between the resources, i.e. some entities are available in one resource and not another, or there may be inconsistent information across resources. While it would be nice not to have to anticipate the types of integration that are needed, our take-away from this, is that at present, it appears we have to accomplish the steps in our pipeline by integrating knowledge from different resources in advance, even though projects such as YAGO have already been working on such integration for at least ten years.Other discourse coherence relations besides expansion are also viable candidates for selecting content for next turns, but finding content that instantiates these relations can be a challenging problem in itself. For example, in casual conversation, it is common to provide opinions and then perhaps further take the initiative and justify them. The justification of an opinion is a type of contingency relation: we describe how we curate content to provide justifications in Section "Mixed Initiative Dialogue" .We have also been able to use the temporal relation in a limited way by drawing on narratively structured sources, such as personal stories in blogs. Since these stories are told in temporal order, we can repurpose the content of these blogs to tell stories, maintaining pre-existing narrative coherence when the system produces a sequence of turns BIBREF33 . However, we posit that there is much more that could be done to make better use of deep semantic discourse relations for recognizing discourse relations and generating coherent conversational turns.Mixed Initiative dialogue is key to a natural conversational interaction BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37 , BIBREF38 , BIBREF2 , and this is even more important for open domain dialogue than it is for task-oriented or information seeking dialogue. One of our primary hypotheses, as described above, is that good models of discourse coherence will help SlugBot identify content that can be used to take the initiative. However, models of discourse coherence have been rarely applied to conversation BIBREF39 , BIBREF40 , BIBREF41 and thus there is considerable work to be done simply in understanding how these relations can be instantiated in dialogue.In addition, a further challenge arises from the fact that both system and user options for dialogue acts are extremely varied at each turn, e.g. user intents can be to provide opinions, give or solicit information, contrast two possibilities, request the system to perform an action, and more. One reasonable taxonomy for the types of dialogue acts that might be available to SlugBot could be based for example on the dialogue act annotations in the Switchboard corpus BIBREF42 .Here, we consider a simple case combining discourse relations and dialogue acts that we have implemented in Slugbot in order to take the initiative in a way that we hoped the user would find interesting. Our aim was to utilize the contingency discourse relation to connect a statement of opinion and its justification. We designed a template containing both arguments of the contingency relation, namely I think $\lbrace entity\rbrace $ is $\lbrace sentiment\rbrace $ because $\lbrace justification\rbrace $ . We construct a table of argument pairs that can instantiate this relation, as shown in Table 3 . This table can be populated by crowd-sourcing or by using search as a pre-processing step.Table 4 illustrates how this is used in our conversations about comics. At Line 6, when the user asks Who is your favorite character?, it is most appropriate to provide an opinion. It is difficult to imagine retrieving search-based data which contains a contextually relevant opinion, but it is even more difficult to imagine that if search had returned such an opinion, that search could be used a second time in order to retrieve a justification for the provided opinion and answer the user's follow-up question in Line 8, Okay why?. The source text for the search would have to be annotated for the type of content that could be used to provide justifications, and search would have to support these types of semantic relations.The current challenges for natural language generation, in our view, arise from the need to combine information from structured and unstructured sources when producing conversational utterances. SlugBot currently uses a combination of pre-written templates, sentence selection, and techniques for telling stories that are based on converting monologic stories to dialogic sequences BIBREF33 .Structured data, when available, can do more than structure a search result: it can also be easier to use within a conversation because it provides the necessary structure needed for high precision natural language generation BIBREF22 , BIBREF43 . More precisely, a small set of generic templates with various slots can be filled with information from structured data sources to insure high quality, accurate responses. These generic templates can be hand crafted, or prepared in advance by learning natural language generation templates automatically from appropriate conversational domain sources such as different types of user-generated content BIBREF44 , BIBREF23 , as illustrated in our justification initiatives above in Section "Mixed Initiative Dialogue" .For general fact-based questions, on the other hand, search content can be used directly. For example, at line 14 in Table 5 when the user asks What was the first movie to feature a vampire?, search provides us with a good response. This introduces however the challenge of updating the discourse context with the right representation of the two movies under discussion, so that they can then be available for follow-on coreference. This is an open problem.It is clear that in order to use a semi-structured approach, we need to determine when to utilize each source. Structured data can be easier to formulate into system responses and can often more easily handle on-topic follow-up questions, but is more limited in scope. An obvious approach, also used in the Watson Jeopardy system BIBREF45 , is to pool responses from both sources and rank them. We have not, to date, collected enough data to build a ranker.Our plan is to apply a combination of reinforcement learning and learning of ranking functions for utterance variants in a particular context to SlugBot conversations as we move forward with our own data collection, outside of the Alexa Prize competition BIBREF46 , BIBREF47 , BIBREF48 , BIBREF49 , BIBREF50 . The first step however is to use the Alexa Prize competition data to learn a Paradise-Open-Domain evaluation function, with additional metrics relevant to open-domain dialogue, e.g. independent variable metrics that predict overall dialogue quality such as response delay, vocabulary diversity, dialogue act sequence n-grams BIBREF51 , conversational depth, number of reprompts BIBREF52 , and other measures that can be automatically logged. Many of the required measures have been used over the last 20 years in Paradise to evaluate task-oriented dialogue systems and they remain highly relevant to overall dialogue quality in open-domain dialogue systems BIBREF53 , BIBREF54 , BIBREF55 . We predict this can potentially improve the overall performance of the system as demonstrated in Table 6 . Here, the structured data is sparse, resulting in an uninteresting response, while search returns a very robust answer. Our Paradise-Open-Domain evaluation function would need to learn to place priority on the result returned by search, through ranking, despite having structured data.For open domain NLG, we have also conducted experiments with neural sequence to sequence approaches using open domain corpora such as film dialogue, Big Bang theory scripts, and open subtitles. These approaches to date do not produce interesting utterances that maintain discourse coherence. It is possible that further curation and semantic annotation of these resources, e.g. by labelling semantic roles and identifying dialogue acts and discourse relations might be helpful, but this could also introduce data sparsity. For example in Switchboard the dialogue act distribution is highly skewed. Integrating information across multiple sources could also be further explored BIBREF33 . Recent work on hybrid neural generation approaches that use knowledge of sentence and discourse planning structures also seem promising BIBREF24 , BIBREF48 , BIBREF56 .In this paper, we describe some of the challenges we encountered building SlugBot, an open domain conversational agent funded by the Amazon Alexa Prize. We have introduced more problems than we have solved, and we have attempted to support our hypothesis that we need richer models of discourse coherence and discourse semantics to allow a conversational agent to take the initiative in open domain conversations. We illustrated how search and structured information can be combined in order for SlugBot to find content to use to take the initiative and respond to the user's utterances. We propose a hybrid approach for language generation that which combines templates to generate responses with sentence selection from search, and we show examples in different domains to demonstrate real-world use cases that make use of our approach. For future work, we plan to bring together resources that provide structured data from different sources into a single, accessible framework, to supply personal assistants with scalable knowledge bases that will power more natural, mixed initiative, and engaging conversations. We believe that it will be possible in the next few years to build conversational agents that can carry on a conversation for 20 minutes about many different topics.

Q: Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?