We study the problem of generating interconnected questions in question-answering style conversations. Compared with previous works which generate questions based on a single sentence (or paragraph), this setting is different in two major aspects: (1) Questions are highly conversational. Almost half of them refer back to conversation history using coreferences. (2) In a coherent conversation, questions have smooth transitions between turns. We propose an end-to-end neural model with coreference alignment and conversation flow modeling. The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history. The conversation flow modeling builds a coherent conversation by starting questioning on the first few sentences in a text passage and smoothly shifting the focus to later parts. Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. The code implementation is released at this https URL.
In this paper, we propose a new setting which is related to the above two lines of research. We consider asking questions grounded in a passage via a question-answering style conversation. Since the questions and answers are in the format of a conversation, questions in our setting are highly conversational and interconnected to conversation history. This setting is challenging because we need to jointly model the attention shifting in the passage and the structure of a conversation @cite_13 . A limitation of the conversation in our setting is that we can only generate a series of interconnected questions according to predefined answers but in a real dialog the questioner can ask different questions according to the answers' response.
@cite_13
2019364517 2888296173 2952389302 2126908276
In two experiments, students overheard two computer-controlled virtual agents discussing four computer literacy topics in dialog discourse and four in monologue discourse. In Experiment 1, the virtual tutee asked a series of deep questions in the dialog condition, but only one per topic in the monologue condition in both studies. In the dialog conditions of Experiment 2, the virtual tutee asked either deep questions, shallow questions, or made comments. In a fourth “dialog” condition, the comments were spoken by the virtual tutor. The discourse spoken by the virtual tutor was identical in the dialog and monologue conditions, except in the fourth dialog condition. In both studies, learners wrote significantly more content and significantly more relevant content in the deep question condition than in the monologue condition. No other differences were significant. Results were discussed in terms of advanced organizers, schema theory, and discourse comprehension theory. Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1 , which is 23.7 points behind human performance (88.8 ), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at this http URL This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level softalignment. Extensive experiments on the large-scale SQuAD and TriviaQA datasets validate the effectiveness of the proposed method. At the time of writing the paper (Jan. 12th 2018), our model achieves the first position on the SQuAD leaderboard for both single and ensemble models. We also achieves state-of-the-art results on TriviaQA, AddSent and AddOne-Sent datasets. Synchronous social Q&A systems exist on the Web and in the enterprise to connect people with questions to people with answers in real-time. In such systems, askers' desire for quick answers is in tension with costs associated with interrupting numerous candidate answerers per question. Supporting users of synchronous social Q&A systems at various points in the question lifecycle (from conception to answer) helps askers make informed decisions about the likelihood of question success and helps answerers face fewer interruptions. For example, predicting that a question will not be well answered may lead the asker to rephrase or retract the question. Similarly, predicting that an answer is not forthcoming during the dialog can prompt system behaviors such as finding other answerers to join the conversation. As another example, predictions of asker satisfaction can be assigned to completed conversations and used for later retrieval. In this paper, we use data from an instant-messaging-based synchronous social Q&A service deployed to an online community of over two thousand users to study the prediction of: (i) whether a question will be answered, (ii) the number of candidate answerers that the question will be sent to, and (iii) whether the asker will be satisfied by the answer received. Predictions are made at many points of the question lifecycle (e.g., when the question is entered, when the answerer is located, halfway through the asker-answerer dialog, etc.). The findings from our study show that we can learn capable models for these tasks using a broad range of features derived from user profiles, system interactions, question setting, and the dialog between asker and answerer. Our research can lead to more sophisticated and more useful real-time Q&A support.