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A Question-Driven News Chatbot What’s The Latest? A Question-driven News Chatbot Philippe Laban John Canny Marti A. Hearst UC Berkeley UC Berkeley UC Berkeley [email protected] [email protected] [email protected] Abstract a set of essential questions and link each question with content that answers it. The motivating idea This work describes an automatic news chat- is: two pieces of content are redundant if they an- bot that draws content from a diverse set of news articles and creates conversations with swer the same questions. As the user reads content, a user about the news. Key components of the system tracks which questions are answered the system include the automatic organization (directly or indirectly) with the content read so far, of news articles into topical chatrooms, inte- and which remain unanswered. We evaluate the gration of automatically generated questions system through a usability study. into the conversation, and a novel method for The remainder of this paper is structured as fol- choosing which questions to present which lows. Section2 describes the system and the con- avoids repetitive suggestions. We describe the algorithmic framework and present the results tent sources, Section3 describes the algorithm for of a usability study that shows that news read- keeping track of the conversation state, Section4 ers using the system successfully engage in provides the results of a usability study evaluation multi-turn conversations about specific news and Section5 presents relevant prior work. stories. The system is publicly available at https:// newslens.berkeley.edu/ and a demonstration 1 Introduction video is available at this link: https://www. Chatbots offer the ability for interactive informa- youtube.com/watch?v=eze9hpEPUgo. tion access, which could be of great value in the news domain. As a user reads through news con- 2 System Description tent, interaction could enable them to ask clarifying This section describes the components of the chat- questions and go in depth on selected subjects. Cur- bot: the content source, the user interface, the sup- rent news chatbots have minimal capabilities, with ported user actions and the computed system an- content hand-crafted by members of news organi- swers. AppendixA lists library and data resources zations, and cannot accept free-form questions. used in the system. To address this need, we design a new approach to interacting with large news collections. We de- 2.1 Content Sources signed, built, and evaluated a fully automated news We form the content for the chatbot from a set of chatbot that bases its content on a stream of news news sources. We have collected an average of articles from a diverse set of English news sources. 2,000 news articles per day from 20 international This in itself is a novel contribution. news sources starting in 2010. The news articles Our second contribution is with respect to the are clustered into stories: groups of news articles scoping of the chatbot conversation. The system about a similar evolving topic, and each story is organizes the news articles into chatrooms, each automatically named (Laban and Hearst, 2017). revolving around a story, which is a set of auto- Some of the top stories at the time of writing are matically grouped news articles about a topic (e.g., shown in Figure 1(a). articles related to Brexit). The third contribution is a method to keep track 2.2 User Interface of the state of the conversation to avoid repetition of The chatbot supports information-seeking: the user information. For each news story, we first generate is seeking information and the system delivers in- 380 Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 380–387 July 5 - July 10, 2020. c 2020 Association for Computational Linguistics (a) Homepage (b) Initiating a Chatroom (c) Chatroom Q&A Figure 1: Screenshots of the news chatbot (a) Homepage lists most recently active chatrooms (Zone 1 is an ex- ample chatroom) (b) Newly opened chatroom: Zone 2 is an event message, Zone 3 the Question Recommendation module, and Zone 4 a text input for user-initiated questions. Event messages are created via abstractive summa- rization. (c) Conversation continuation with Q&A examples. Sentences shown are extracted from original articles, whose sources are shown. Answers to questions are bolded. formation in the form of news content. Because of the difference in respective roles, we The homepage (Figure 1(a)) lists the most active expect user messages to be shorter than system stories, and a user can select a story to enter its responses, which we aim to be around 30 words. respective chatroom (Figure 1(b)). The separation into story-specific rooms achieves two objectives: 2.3 User Actions (1) clarity to the user, as the chatrooms allow the During the conversation, the user can choose user to exit and enter chatrooms to come back to among different kinds of actions. conversations, and (2) limiting the scope of each Explore the event timeline. A chatroom con- dialogue is helpful from both a usability and a tech- versation starts with the system showing the two nical standpoint, as it helps reduce ambiguity and most recent event messages of the story (Fig- search scope. For example, answering a question ure 1(b)). These messages give minimal context to like: “What is the total cost to insurers so far?” the user necessary to start a conversation. When is easier when knowing the scope is the Australia the event timeline holds more than two events, a Fires, compared to all of news. “See previous events” button is added at the top of Articles in a story are grouped into events, corre- the conversation, allowing the user to go further sponding to an action that occurred in a particular back in the event timeline of the story. time and place. For each event, the system forms Clarify a concept. The user can ask a clarifi- an event message by combining the event’s news cation question regarding a person or organization article headlines generated by an abstractive sum- (e.g., Who is Dennis Muilenburg?), a place (e.g., marizer model (Laban et al., 2020). Where is Lebanon?) or an acronym (e.g., What Zone 2 in Figure 1(b) gives an example of an does NATO stand for?). For a predetermined list event message. The event messages form a chrono- of questions, the system will see if an appropriate logical timeline in the story. Wikipedia entry exists, and will respond with the 381 Since October in Australia, fires scorched Conversation Start Mid Conversation more than 10.3 million hectares and 27 P P people have been killed Q P Q P Q P Q P what else should I know Q P Conversation Q P Q P advances Q P Q P Q P The fires, which have been raging since October, have killed at least 24 people and Q P Q P burned 10 million hectares Questions P Questions P Paragraphs Paragraphs you said that already... Legend Q Unanswered Question Q Answered question P Informative Paragraph P Read Paragraph P Uninformative Paragraph Figure 2: Example of repetition from the system. Re- peating facts with different language is undesirable in a Figure 3: Conversation state is tracked with the P/Q news chatbot. We introduce a novel question tracking graph. As the conversation advances, the system keeps method that attempts to minimize repetition. track of answered questions. Any paragraph that does not answer a new question is discarded. Questions that are not answered yet are recommended. first two paragraphs of the Wikipedia page. For geographical entities, the system additionally re- sponds with a geographic map when possible. content are redundant if they answer the same ques- Ask an open-ended question. A text box (Zone tions. In the example of Figure2, both system 4 in Figure 1(b)) can be used to ask any free-form messages answer the same set of questions, namely: question about the story. A Q&A system described “When did the fires start?”, “How many people have in Section3 attempts to find the answer in any died?” and “How many hectares have burned?”, paragraph of any news article of the story. If the and can therefore be considered redundant. Q&A system reaches a confidence level about at Our procedure to track the knowledge state least one paragraph containing an answer to the of a news conversation consists of the following question, the chatbot system answers the question steps: (1) generate candidate questions spanning using one of the paragraphs. In the system reply the knowledge in the story, (2) build a graph con- the Q&A selected answer is bolded. Figure 1(c) necting paragraphs with questions they answer, (3) shows several Q&A exchanges. during a conversation, use the graph to track what Select a recommended question. A list of questions have been answered already, and avoid three questions generated by the algorithm de- using paragraphs that do not answer new questions. scribed in Section3 is suggested to the user at the Question Candidate Generation. We fine-tune bottom of the conversation (Zone 3 in Figure 1(b)). a GPT2 language model (Radford et al., 2019) on Clicking on a recommended questions corresponds the task of question generation using the SQuAD to asking the question in free-form. However, be- 2.0 dataset (Rajpurkar et al., 2018). At training, the cause recommended questions are known in ad- model reads a paragraph from the training set, and vance, we pre-compute their answers to minimize learns to generate a question associated with the user waiting times. paragraph. For each paragraph in each article of the story (the paragraph set), we use beam search to 3 Conversation State generate K candidate questions.
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