Conversational Information Seeking (CIS) is an emerging paradigm for knowledge acquisition and exploratory search. Traditional web search interfaces enable easy exploration of entities, but this is limited in conversational settings due to the limited-bandwidth interface. This paper explore ways to rewrite answers in CIS, so that users can understand them without having to resort to external services or sources. Specifically, we focus on salient entities—entities that are central to understanding the answer. As our first contribution, we create a dataset of conversations annotated with entities for saliency. Our analysis of the collected data reveals that the majority of answers contain salient entities. As our second contribution, we propose two answer rewriting strategies aimed at improving the overall user experience in CIS. One approach expands answers with inline definitions of salient entities, making the answer self-contained. The other approach complements answers with follow-up questions, offering users the possibility to learn more about specific entities. Results of a crowdsourcing-based study indicate that rewritten answers are clearly preferred over the original ones. We also find that inline definitions tend to be favored over follow-up questions, but this choice is highly subjective, thereby providing a promising future direction for personalization.

Clarifying underlying user information needs by asking clarifying questions is an important feature of modern conversational search systems. However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In our recent work, we proposed an approach to tackle these issues with a user simulator,USi. Given a description of an information need,USiis capable of automatically answering clarifying questions about the topic throughout the search session. However, while the answers generated byUSiare both in line with the underlying information need and in natural language, a deeper understanding of such utterances is lacking. Thus, in this work, we explore utterance formulation of large language model (LLM)–based user simulators. To this end, we first analyze the differences betweenUSi, based on GPT-2, and the next generation of generative LLMs, such as GPT-3. Then, to gain a deeper understanding of LLM-based utterance generation, we compare the generated answers to the recently proposed set of patterns of human-based query reformulations. Finally, we discuss potential applications as well as limitations of LLM-based user simulators and outline promising directions for future work on the topic.

This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS ) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack of system-user conversational interaction data. To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. Specifically, we develop a user simulator, dubbed ConvSim, that, once initialized with an information need description, is capable of providing feedback to system's responses, as well as answering potential clarifying questions. Our experiments on a wide variety of state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to 16% retrieval performance increase in terms of nDCG@3. Moreover, we observe consistent improvements as the number of feedback rounds increases (35% relative improvement in terms of nDCG@3 after three rounds). This points to a research gap in the development of specific feedback processing modules and opens a potential for significant advancements in CS. To support further research in the topic, we release over 30 000 transcripts of system-simulator interactions based on well-established CS datasets.

Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search system. However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In this paper, we propose a conversational User Simulator, called USi, for automatic evaluation of such conversational search systems. Given a description of an information need, USi is capable of automatically answering clarifying questions about the topic throughout the search session. Through a set of experiments, including automated natural language generation metrics and crowdsourcing studies, we show that responses generated by USi are both inline with the underlying information need and comparable to human-generated answers. Moreover, we make the first steps towards multi-turn interactions, where conversational search systems asks multiple questions to the (simulated) user with a goal of clarifying the user need. To this end, we expand on currently available datasets for studying clarifying questions, i.e., Qulac and ClariQ, by performing a crowdsourcing-based multi-turn data acquisition. We show that our generative, GPT2-based model, is capable of providing accurate and natural answers to unseen clarifying questions in the single-turn setting and discuss capabilities of our model in the multi-turn setting. We provide the code, data, and the pre-trained model to be used for further research on the topic.

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.However, effective aggregation of relevant information in the document remains a challenging research question.Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies.Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph.We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning.Specifically, our model achieves an F 1 score of 59.05 on a large-scale documentlevel dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset.Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard the International Space Station (GEROS-ISS) experiment consists of the installation of a GNSS reflectometry (GNSS-R) instrument on the International Space Station (ISS). Due to the complex structure of ISS, the GEROS-ISS instrument suffers from multipath effects. The collected signals are not only from received directly, but also after multiple reflections in the ISS structure. This work analyzes the multipath effect of the GEROS-ISS instrument based on the ISS structure. Using the ray tracing simulation, all rays reaching the instrument after one or two reflections are computed, as well as the corresponding additional delays. This information, together with the direction of arrival to the antenna arrays (up and down-looking), and the co- and cross-polar antenna patterns is used to assess the impact of multipath on the waveforms are demonstrated.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard International Space Station (GEROS-ISS) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA) for installation at the ISS. This international proposal was the only one selected for further studies by ESA out of ~25 submitted ones. In this work, the assessment of the instrument performance for the near-nadir altimetry (GNSS-R) mode is assessed, including the effects of multi-path in the ISS structure, the electromagnetic-bias, and the orbital height decay. In the absence of ionospheric scintillations, the altimetry rms error is20 dB at equatorial regions, mainly after sunset, which will seriously degrade the altimetry and the scatterometry performances of the instrument.