Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.
University of California, Santa Barbara（加州大学圣巴巴拉分校）；J.P. Morgan（摩根大通）
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great ch…
Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms…
MPI for Informatics（马克斯·普朗克信息学研究所）；Amazon Alexa（亚马逊Alexa）
Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically…
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize（阿霍德德尔海兹）
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and t…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；RMIT University（皇家墨尔本理工大学）；Rutgers University（罗格斯大学）；Alibaba Group（阿里巴巴集团）
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are a…
IASI-CNR（意大利国家研究委员会信息分析与系统研究所）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；University of Amsterdam（阿姆斯特丹大学）
The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conversational systems. Oftentimes, the users fulfill their information need using smartphones and home assistants. This has revolutionized the way users access online information, thus posing new challenges compared to traditional search and recommendation. The first edition of MICROS will have a particular focus on mixed-initiative conversational systems. Indeed, conversational systems need to be proactive, proposing n…
Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). For some of these systems that leverage a shared NLU ontology (e.g., a centralized intent/slot schema), there exists a separate skill routing component to correctly route a request to an appropriate skill, which is either a first-party or third-party application that actually executes on a user request. The skill routing component is needed as there are thousands of skills t…
University of Lincoln（林肯大学）；Samsung Research（三星研究院）
The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled investigations into optimal context windows are urgently needed given that the amount of dialogue history and corresponding representations can play an important role in the overall performance of a conversational system. This paper studies the amount of history required by conversational agents for reliably predicting dialogue rewards. The task of dialogue reward prediction is chosen for investigating the effects of v…
StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control
NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China（计算机科学与工程学院自然语言处理实验室，东北大学，沈阳，中国）；NiuTrans Research, Shenyang, China（尼乌透研究，沈阳，中国）
Ecole Polytechnique de Montréal（蒙特利尔综合理工学院）；Element AI（元素人工智能公司）；Microsoft Research Montreal（微软研究院蒙特利尔分院）；Université de Montréal（蒙特利尔大学）；Mila（米拉）；HEC Montréal（蒙特利尔高等商学院）
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we hav…
For spoken dialog systems to conduct fluid conversational interactions with users, the systems must be sensitive to turn-taking cues produced by a user. Models should be designed so that effective decisions can be made as to when it is appropriate, or not, for the system to speak. Traditional end-of-turn models, where decisions are made at utterance end-points, are limited in their ability to model fast turn-switches and overlap. A more flexible approach is to model turn-taking in a continuous manner using RNNs, where the system predicts speech probability scores for discrete frames within a f…
University of Southern California（南加州大学）；Carnegie Mellon University（卡内基梅隆大学）
Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design…
University of Marburg（马尔堡大学）；Technical University of Darmstadt（达姆施塔特工业大学）
There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations d…
Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models
School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；College of Computing and Informatics, Saudi Electronic University（沙特电子大学计算与信息学院）；School of Biosciences, Cardiff University（卡迪夫大学生物科学学院）
Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection
Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院粤港澳人机协同智能系统联合实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Good Mood Health Industry Group Co., Ltd（好心情健康产业集团有限公司）；Shanghai Jiao Tong University（上海交通大学）
