Conversational search has been regarded as the next-generation search paradigm. Constrained by data scarcity, most existing methods distill the well-trained ad-hoc retriever to the conversational retriever. However, these methods, which usually initialize parameters by query reformulation to discover contextualized dependency, have trouble in understanding the dialogue structure information and struggle with contextual semantic vanishing. In this paper, we propose \fullmodel (\model) which is a new post-training paradigm with three self-supervised tasks to efficiently initialize the conversational search model to enhance the dialogue structure and contextual semantic understanding. Furthermore, the \model can be plugged into most of the existing conversational models to boost their performance. To verify the effectiveness of our proposed method, we apply the conversational encoder post-trained by \model on the conversational search task using two benchmark datasets: CAsT-19 and CAsT-20. Extensive experiments that our \model can boost the performance of several existing conversational search methods. Our source code is available at \url{https://github.com/morecry/SSP}.
Athens University of Economics and Business（雅典经济与商业大学）；Stockholm University（斯德哥尔摩大学）；Télécom Paris, Institut Polytechnique de Paris（巴黎电信学院，巴黎理工学院）；Google Jigsaw；Google（谷歌）
User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on existing datasets will also tend to disregard context, making the detection of context-sensitive toxicity harder when it does occur. We construct and publicly release a dataset of 10,000 posts with two kinds of toxicity labels: (i) annotators considered each post with the previous one as context; and (ii) annotators had no additional context. Based on this, we introduce a new task, context sensitivity estimation, which aims to identify …
UC San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）
A limitation of current neural dialog models is that they tend to suffer from a lack of specificity and informativeness in generated responses, primarily due to dependence on training data that covers a limited variety of scenarios and conveys limited knowledge. One way to alleviate this issue is to extract relevant knowledge from external sources at decoding time and incorporate it into the dialog response. In this paper, we propose a post-hoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initia…
Seoul National University（首尔大学）；North China Univ. of Technology（北方工业大学）；Art Center Nabi（Nabi艺术中心）；Graduate School of Engineering Practice（工程实践研究生院）
The development of natural language processing algorithms and the explosive growth of conversational data are encouraging researches on the human-computer conversation. Still, getting qualified conversational data on a large scale is difficult and expensive. In this paper, we verify the feasibility of constructing a data-driven chatbot with processed online community posts by using them as pseudo-conversational data. We argue that chatbots for various purposes can be built extensively through the pipeline exploiting the common structure of community posts. Our experiment demonstrates that chat…
iFLYTEK Research（科大讯飞研究院）；Harbin Institute of Technology（哈尔滨工业大学）；iFLYTEK AI Research (Hebei)（科大讯飞人工智能研究院（河北））
Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs $ \left\langle{post, reply} \right\rangle$ to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply. To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, the…
School of Computer Science & Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Data Science Lab, JD.com（京东数据科学实验室）
Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds t…
Playpen: An Environment for Exploring Learning Through Conversational Interaction
University of Potsdam（波恩大学）；University of Trento（特伦特大学）；Saarland University（萨尔大学）；ETH Zurich（苏黎世联邦理工学院）；Amsterdam UMC（阿姆斯特丹医疗中心）；Free University of Bozen Bolzano（博兹纳自由大学）；University of Amsterdam（阿姆斯特丹大学）；Heriot-Watt University（赫里奥特-瓦特大学）；DFKI（德意志联邦研究所）；UCL（伦敦大学学院）；University of Edinburgh（爱丁堡大学）
Can a robot be a personal dating coach? Even with the increasing amount of conversational data on the internet, the implementation of conversational robots remains a challenge. In particular, a detailed and professional counseling log is expensive and not publicly accessible. In this paper, we develop a robot dating coaching system leveraging corpus from online communities. We examine people's perceptions of the dating coaching robot with a dialogue module. 97 participants joined to have a conversation with the robot, and 30 of them evaluated the robot. The results indicate that participants t…
The Levers of Political Persuasion with Conversational AI
UK AI Security Institute（英国人工智能安全研究所）；University of Oxford（牛津大学）；The London School of Economics and Political Science（伦敦政治经济学院）；Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Recent years have seen growing interest in conversational agents, such as chatbots, which are a very good fit for automated customer support because the domain in which they need to operate is narrow. This interest was in part inspired by recent advances in neural machine translation, esp. the rise of sequence-to-sequence (seq2seq) and attention-based models such as the Transformer, which have been applied to various other tasks and have opened new research directions in question answering, chatbots, and conversational systems. Still, in many cases, it might be feasible and even preferable to …
