More Like this

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))

   null (Ed.)

   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 the 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. 

   more » « less
2. Conversational Product Search Based on Negative Feedback

   https://doi.org/10.1145/3357384.3357939  

   Bi, Keping ; Ai, Qingyao ; Zhang, Yongfeng ; Croft, W. Bruce ( November 2019 , Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19)

   Intelligent assistants change the way for people to interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on certain aspects of the ideal products to clarify the users' needs. Previous work proposed to ask users the exact characteristics of their ideal items before showing results. However, users may not have clear ideas about what an ideal item should be like, especially when they have not seen any items. So it is more feasible to facilitate the conversational search by showing example items and asking for feedback instead. In addition, when the users provide negative feedback for the presented items, it is easier to collect their detailed feedback on certain properties (aspect-value pairs) of the non-relevant items. By breaking down the item-level negative feedback to fine-grained feedback on aspect-value pairs, more information is available to help clarify users' intents. So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration. We then propose an aspect-value likelihood model to incorporate both positive and negative feedback on fine-grained aspect-value pairs of the non-relevant items. Experimental results show that our model is significantly better than state-of-art product search baselines without using feedback and baselines using item-level negative feedback. 

   more » « 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, 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. 

   more » « less
4. 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. In 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. 

   more » « less
5. Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search

   https://doi.org/10.1145/3397271.3401061  

   Hashemi, Helia ; Zamani, Hamed ; Croft, W. Bruce ( July 2020 , Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020))

   null (Ed.)

   Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, in particular, conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying question have been recently studied in the literature. However, accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we propose a neural network model based on a novel attention mechanism, called multi source attention network. Our model learns a representation for a user-system conversation that includes clarifying questions. In more detail, with the help of multiple information sources, our model weights each term in the conversation. In our experiments, we use two separate external sources, including the top retrieved documents and a set of different possible clarifying questions for the query. We implement the proposed representation learning model for two downstream tasks in conversational search; document retrieval and next clarifying question selection. We evaluate our models using a public dataset for search clarification. Our experiments demonstrate significant improvements compared to competitive baselines. 

   more » « less