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1. Asking Clarifying Questions Based on Negative Feedback in Conversational Search

   https://doi.org/10.1145/3471158.3472232  

   Ai, Q. ; Bi, K. ; Croft, W. B. ( July 2021 , Proceedings of the 7th ACM International Conference on the Theory of Information Retrieval (ICTIR 2021))

   Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users’ search intents. This, however, can take significant effort to answer a series of questions starting with “what/why/how”. To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about themore »previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks.« less
2. Attentive History Selection for Conversational Question Answering

   https://doi.org/10.1145/3357384.3357905  

   Qu, Chen ; Yang, Liu ; Qiu, Minghui ; Zhang, Yongfeng ; Chen, Cen ; Croft, W. Bruce ; Iyyer, Mohit ( November 2019 , Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19)

   Conversational AI is a rapidly developing research field in both industry and academia. As one of the major branches of conversational AI, question answering and conversational search has attracted significant attention of researchers in the information retrieval community. It has been a long overdue feature for search engines or conversational assistants to retrieve information iteratively and interactively in a conversational manner. Previous work argues that conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. In this setting, one of the major challenges is to leverage the conversation history to understand and answer the current question. Inmore »this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT (Bidirectional Encoder Representations from Transformers) in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding. The complete implementation of our model will be open-sourced.« less
3. Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

   https://doi.org/10.1145/3331184.3331265  

   Aliannejadi, Mohammad ; Zamani, Hamed ; Crestani, Fabio ; Croft, W. Bruce ( July 2019 , Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR'19)

   Users often fail to formulate their complex information needs in a single query. As a consequence, they need to scan multiple result pages and/or reformulate their queries, which is a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions from the users to clarify their information needs. Asking clarifying questions is especially important in information-seeking conversational systems, since they can only return a limited number (often only one) of results. In this paper, we formulate the task of asking clarifying questions in open-domain information retrieval. We propose an offline evaluation methodology for the task. In this research,more »we create a dataset, called Qulac, through crowdsourcing. Our dataset is based on the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets. Our experiments on an oracle model demonstrate that asking only one good question leads to over 100% retrieval performance improvement, which clearly demonstrates the potential impact of the task. We further propose a neural model for selecting clarifying question based on the original query and the previous question-answer interactions. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.« less
4. BERT with History Answer Embedding for Conversational Question Answering

   https://doi.org/10.1145/3331184.3331341  

   Qu, Chen ; Yang, Liu ; Qiu, Minghui ; Croft, W. Bruce ; Zhang, Yongfeng ; Iyyer, Mohit ( July 2019 , Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR'19)

   Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to understand the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete settingmore »of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings. We show that history prepending methods degrade dramatically when given a long conversation history while our method is robust and shows advantages under such a situation, which provides new insights into conversation history modeling in ConvQA.« less
5. Towards Conversational Search and Recommendation: System Ask, User Respond

   https://doi.org/10.1145/3269206.3271776  

   Zhang, Yongfeng ; Chen, Xu ; Ai, Qingyao ; Yang, Liu ; Croft, W. Bruce ( October 2018 , Proceedings of the 27th ACM International Conference on Information and Knowledge Management)

   Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications, such as Amazon Echo, Google Now, Microsoft Cortana, Apple Siri, and many others. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the IR community has begun to focus on Conversational Search. Conversational search based on user-system dialog exhibits major differences from conventional search in that 1) the user and system can interact for multiple semantically coherent rounds on a task through natural language dialog, and 2) it becomes possible for the systemmore »to understand user needs or to help users clarify their needs by asking appropriate questions from the users directly. In this paper, we propose and evaluate a unified conversational search framework. Specifically, we define the major components for conversational search, assemble them into a unified framework, and test an implementation of the framework using a conversational product search scenario in Amazon. To accomplish this, we propose the Multi-Memory Network (MMN) architecture, which is end-to-end trainable based on large-scale collections of user reviews in e-commerce. The system is capable of asking aspect-based questions in the right order so as to understand user needs, while (personalized) search is conducted during the conversation and results are provided when the system feels confident. Experiments on real-world user purchasing data verified the advantages of conversational search against conventional search algorithms in terms of standard evaluation measures such as NDCG.« less