We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12%. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models.

We describe a zero shot approach to intent classification that allows for the identification of intents that were not present during training. Our approach makes use of a Long-short Term Memory neural network to encode user queries and intents and uses these encodings to score previously unseen intents based on their semantic similarity to the queries. We test our model on intent classification in a personal digital assistant and show an improvement of 15% over a strong baseline. We also investigate the effect of adding a few training samples for the previously unseen intents in a few shot learning setting and show improvements of up to 16% over the baseline method.

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.

The Web contains many APIs that could be combined in countless ways to enable Intelligent Assistants to complete all sorts of tasks. We propose a method to automatically produce task completion flows from a collection of these APIs by combining them in a graph and automatically extracting paths from the graph for task completion. These paths chain together API calls and use the output of executed APIs as inputs to others. We automatically extract these paths from an API graph in response to a user query and then rank the paths by the likelihood of them leading to user satisfaction. We apply our approach for task completion in the email and calendar domains and show how it can be used to automatically create task completion flows.

Intelligent assistants are increasingly being used on smart speaker devices, such as Amazon Echo, Google Home, Apple Homepod, and Harmon Kardon Invoke with Cortana. Typically, user satisfaction measurement relies on user interaction signals, such as clicks and scroll movements, in order to determine if a user was satisfied. However, these signals do not exist for smart speakers, which creates a challenge for user satisfaction evaluation on these devices. In this paper, we propose a new signal, user intent, as a means to measure user satisfaction. We propose to use this signal to model user satisfaction in two ways: 1) by developing intent sensitive word embeddings and then using sequences of these intent sensitive query representations to measure user satisfaction; 2) by representing a user's interactions with a smart speaker as a sequence of user intents and thus using this sequence to identify user satisfaction. Our experimental results indicate that our proposed user satisfaction models based on the intent-sensitive query representations have statistically significant improvements over several baselines in terms of common classification evaluation metrics. In particular, our proposed task satisfaction prediction model based on intent-sensitive word embeddings has a 11.81% improvement over a generative model baseline and 6.63% improvement over a user satisfaction prediction model based on Skip-gram word embeddings in terms of the F1 metric. Our findings have implications for the evaluation of Intelligent Assistant systems.

Task and session identification is a key element of system evaluation and user behavior modeling in Intelligent Assistant (IA) systems. However, identifying task and sessions for IAs is challenging due to the multi-task nature of IAs and the differences in the ways they are used on different platforms, such as smart-phones, cars, and smart speakers. Furthermore, usage behavior may differ among users depending on their expertise with the system and the tasks they are interested in performing. In this study, we investigate how to identify tasks and sessions in IAs given these differences. To do this, we analyze data based on the interaction logs of two IAs integrated with smart-speakers. We fit Gaussian Mixture Models to estimate task and session boundaries and show how a model with 3 components models user interactivity time better than a model with 2 components. We then show how session boundaries differ for users depending on whether they are in a learning-phase or not. Finally, we study how user inter-activity times differs depending on the task that the user is trying to perform. Our findings show that there is no single task or session boundary that can be used for IA evaluation. Instead, these boundaries are influenced by the experience of the user and the task they are trying to perform. Our findings have implications for the study and evaluation of Intelligent Agent Systems.

Queries for which there are no clicks are known as abandoned queries. Differentiating between good and bad abandonment queries has become an important task in search engine evaluation since it allows for better measurement of search engine features that do not require users to click. Examples of these features include answers on the SERP and detailed Web result snippets. In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment. To do this, we study the behavior patterns on a labeled dataset of abandoned queries and find that they differ in several ways, such as in the number of user interactions and the nature of those interactions. Based on this insight, we frame good abandonment detection as a sequence classification problem. We use a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to model the sequence of user interactions and show that it performs significantly better than other baselines when detecting good abandonment, achieving 71% accuracy. Our findings have implications for search engine evaluation.

We present a recursive pseudo relevance feedback strategy for improving retrieval performance in similarity search. The strategy recursively searches on search results returned for a given query and produces a tree that is used for ranking. Experiments on the Reuters 21578 and WebKB datasets show how the strategy leads to a significant improvement in similarity search performance.

Answers on mobile search result pages have become a common way to attempt to satisfy users without them needing to click on search results. Many different types of answers exist, such as weather, flight and currency answers. Understanding the effect that these different answer types have on mobile user behavior and how they contribute to satisfaction is important for search engine evaluation. We study these two aspects by analyzing the logs of a commercial search engine and through a user study. Our results show that user click, abandonment and engagement behavior differs depending on the answer types present on a page. Furthermore, we find that satisfaction rates differ in the presence of different answer types with simple answer types, such as time zone answers, leading to more satisfaction than more complex answers, such as news answers. Our findings have implications for the study and application of user satisfaction for search systems.

Scholarly documents contain many data entities, such as titles, authors, affiliations, figures, and tables. These entities can be used to enhance digital library services through enhanced metadata and enable the development of new services and tools for interacting with and exploring scholarly data. However, in a world of scholarly big data, extracting these entities in a scalable, efficient and accurate manner can be challenging. In this tutorial, we introduce the broad field of information extraction for scholarly digital libraries. Drawing on our experience in running the Cite-SeerX digital library, which has performed information extraction on over 7 million academic documents, we argue for the need for automatic information extraction, describe different approaches for performing information extraction, present tools and datasets that are readily available, and describe best practices and areas of research interest.

Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered as leading to user dissatisfaction. However, there are many cases where a user may not click on any search result page (SERP) but still be satisfied. This scenario is referred to as good abandonment and presents a challenge for most approaches measuring search satisfaction, which are usually based on clicks and dwell time. The problem is exacerbated further on mobile devices where search providers try to increase the likelihood of users being satisfied directly by the SERP. This paper proposes a solution to this problem using gesture interactions, such as reading times and touch actions, as signals for differentiating between good and bad abandonment. These signals go beyond clicks and characterize user behavior in cases where clicks are not needed to achieve satisfaction. We study different good abandonment scenarios and investigate the different elements on a SERP that may lead to good abandonment. We also present an analysis of the correlation between user gesture features and satisfaction. Finally, we use this analysis to build models to automatically identify good abandonment in mobile search achieving an accuracy of 75%, which is significantly better than considering query and session signals alone. Our findings have implications for the study and application of user satisfaction in search systems.

Abandonment in web search has been widely used as a proxy to measure user satisfaction. Initially it was considered a signal of dissatisfaction, however with search engines moving towards providing answer-like results, a new category of abandonment was introduced and referred to as Good Abandonment. Predicting good abandonment is a hard problem and it was the subject of several previous studies. All those studies have focused, though, on predicting good abandonment in offline settings using manually labeled data. Thus, it remained a challenge how to have an online metric that accounts for good abandonment. In this work we describe how a search success metric can be augmented to account for good abandonment sessions using a machine learned metric that depends on user's viewport information. We use real user traffic from millions of users to evaluate the proposed metric in an A/B experiment. We show that taking good abandonment into consideration has a significant effect on the overall performance of the online metric.

We present a framework for constructing a specific type of knowledge graph, a concept map from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems independently. We propose a framework that jointly optimizes these sub-problems and investigates methods that identify concept relationships. Experiments on concept maps that are manually extracted in six educational areas (computer networks, macroeconomics, precalculus, databases, physics, and geometry) show that our model outperforms supervised learning baselines that solve the two sub-problems separately. Moreover, we observe that incorporating textbook information helps with concept map extraction.

There is a rapid growth in the use of voice-controlled intelligent personal assistants on mobile devices, such as Microsoft's Cortana, Google Now, and Apple's Siri. They significantly change the way users interact with search systems, not only because of the voice control use and touch gestures, but also due to the dialogue-style nature of the interactions and their ability to preserve context across different queries. Predicting success and failure of such search dialogues is a new problem, and an important one for evaluating and further improving intelligent assistants. While clicks in web search have been extensively used to infer user satisfaction, their significance in search dialogues is lower due to the partial replacement of clicks with voice control, direct and voice answers, and touch gestures.In this paper, we propose an automatic method to predict user satisfaction with intelligent assistants that exploits all the interaction signals, including voice commands and physical touch gestures on the device. First, we conduct an extensive user study to measure user satisfaction with intelligent assistants, and simultaneously record all user interactions. Second, we show that the dialogue style of interaction makes it necessary to evaluate the user experience at the overall task level as opposed to the query level. Third, we train a model to predict user satisfaction, and find that interaction signals that capture the user reading patterns have a high impact: when including all available interaction signals, we are able to improve the prediction accuracy of user satisfaction from 71% to 81% over a baseline that utilizes only click and query features.

We describe BBookX, a web-based tool that uses a human-computing approach to facilitate the creation of open source textbooks. The goal of BBookX is to create a system that can search various Open Educational Resource (OER) repositories such as Wikipedia, based on a set of user-generated criteria, and return various resources that can be combined, remixed, and re-used to support specific learning goals. As BBookX is a work-in-progress, we are in the midst of a design-based research study, where user testing guided multiple rounds of iteration in the design of the user interface (UI) as well as the query engine. From an interface perspective, the challenges we present are the matching of the UI to users' mental models from similar systems, as well as educating users how to best work with the algorithms in an iterative manner to find and refine content for inclusion into open textbooks.

We demonstrate BBookX, a novel system that auto-matically builds in collaboration with a user online openbooks by searching open educational resources (OER).This system explores the use of retrieval technologies todynamically generate zero-cost materials such as text-books for personalized learning.

Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices. They introduce a significant change in information access, not only by introducing voice control and touch gestures but also by enabling dialogues where the context is preserved. This raises the need for evaluation of their effectiveness in assisting users with their tasks. However, in order to understand which type of user interactions reflect different degrees of user satisfaction we need explicit judgements. In this paper, we describe a user study that was designed to measure user satisfaction over a range of typical scenarios of use: controlling a device, web search, and structured search dialogue. Using this data, we study how user satisfaction varied with different usage scenarios and what signals can be used for modeling satisfaction in the different scenarios. We find that the notion of satisfaction varies across different scenarios, and show that, in some scenarios (e.g. making a phone call), task completion is very important while for others (e.g. planning a night out), the amount of effort spent is key. We also study how the nature and complexity of the task at hand affects user satisfaction, and find that preserving the conversation context is essential and that overall task-level satisfaction cannot be reduced to query-level satisfaction alone. Finally, we shed light on the relative effectiveness and usefulness of voice-controlled intelligent agents, explaining their increasing popularity and uptake relative to the traditional query-response interaction.