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Neural sequence labeling is widely adopted for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and semantic parsing. Recent advances with large-scale pre-trained language models have shown remarkable success in these tasks when fine-tuned on large amounts of task-specific labeled data. However, obtaining such large-scale labeled training data is not only costly, but also may not be feasible in many sensitive user applications due to data access and privacy constraints. This is exacerbated for sequence labeling tasks requiring such annotations at token-level. In this work, we develop techniques to address the label scarcity challenge for neural sequence labeling models. Specifically, we propose a meta self-training framework which leverages very few manually annotated labels for training neural sequence models. While self-training serves as an effective mechanism to learn from large amounts of unlabeled data via iterative knowledge exchange -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels. Extensive experiments on six benchmark datasets including two for massive multilingual NER and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method. With only 10 labeled examples for each class in each task, the proposed method achieves 10% improvement over state-of-the-art methods demonstrating its effectiveness for limited training labels regime.

Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue. We employ a generative model to iteratively sample a subset of challenging negative samples to fool our classification model. Both models are alternatively optimized using REINFORCE algorithm. The proposed method is completely different from previous ones, where negative samples in training set are directly used or uniformly down-sampled. Further, we propose using Multi-scale Matching which explicitly inspects the correlation between words and ngrams of different levels of granularity. We evaluate the proposed method on SemEval 2016 and SemEval 2017 datasets and achieves state-of-the-art or similar performance.

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines., Accepted to NAACL 2021 (Long Paper)

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs., ACL 2021

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit., Comment: NAACL 2021

The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an under-studied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL., 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel prediction tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERT-LARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322., Accepted to NAACL 2021. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322

Background: Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or crash, one of the common practices of software engineers is to search for information about the associated error or exception traces on the internet. Aims: In this paper, we analyze query logs from Bing to carry out a large scale study of software exceptions. To the best of our knowledge, this is the first large scale study to analyze how Web search is used to find information about exceptions. Method: We analyzed about 1 million exception related search queries from a random sample of 5 billion web search queries. To extract exceptions from unstructured query text, we built a novel machine learning model. With the model, we extracted exceptions from raw queries and performed popularity, effort, success, query characteristic and web domain analysis. We also performed programming language-specific analysis to give a better view of the exception search behavior. Results: Using the model with an F1-score of 0.82, our study identifies most frequent, most effort-intensive, or less successful exceptions and popularity of community Q&A sites. Conclusion: These techniques can help improve existing methods, documentation and tools for exception analysis and prediction. Further, similar techniques can be applied for APIs, frameworks, etc.

We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form. In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance. We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction. While we estimated human correction accuracy is 81.5%, our best model achieves only 25.1%, which leaves a large gap for improvement in future research. SPLASH is publicly available at https://aka.ms/Splash_dataset., ACL 2020

Personalized document recommendation systems aim to provide users with a quick shortcut to the documents they may want to access next, usually with an explanation about why the document is recommended. Previous work explored various methods for better recommendations and better explanations in different domains. However, there are few efforts that closely study how users react to the recommended items in a document recommendation scenario. We conducted a large-scale log study of users’ interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com. Our analysis reveals a number of factors, including display position, file type, authorship, recency of last access, and most importantly, the recommendation explanations, that are associated with whether users will recognize or open the recommended documents. Moreover, we specifically focus on explanations and conduct an online experiment to investigate the influence of different explanations on user behavior. Our analysis indicates that the recommendations help users access their documents significantly faster, but sometimes users miss a recommendation and resort to other more complicated methods to open the documents. Our results suggest opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents.

Recommender systems are commonly used in entertainment, news, e-commerce, and social media. Document recommendation is a new and under-explored application area, in which both re-finding and discovery of documents need to be supported. In this paper we provide an initial exploration of users' experience with recommended documents, with a focus on how prior interactions influence recognition and interest. Through a field study of more than 100 users, we investigate the effects of past interactions with recommended documents on users' recognition of, prior intent to open, and interest in the documents. We examined different presentations of interaction history, and the recency and richness of prior interaction. We found that presentation only influenced recognition time. Our findings also indicate that people are more likely to recognize documents they had accessed recently and to do so more quickly. Similarly, documents that people had interacted with more deeply were also more frequently and quickly recognized. However, people were more interested in older documents or those with which they had less involved interactions. This finding suggests that in addition to helping users quickly access documents they intend to re-find, document recommendation can add value in helping users discover other documents. Our results offer implications for designing document recommendation systems that help users fulfil different needs.

Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence on the bias in transfer learning. We further provide recommendations for using the multilingual word representations for downstream tasks.

Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks. However, the huge size of these models could be a deterrent to use them in practice. Some recent and concurrent works use knowledge distillation to compress these huge models into shallow ones. In this work we study knowledge distillation with a focus on multi-lingual Named Entity Recognition (NER). In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations that is agnostic of teacher architecture and show that it outperforms strategies employed in prior works. Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few. We show that our approach leads to massive compression of MBERT-like teacher models by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95% of its F1-score for NER over 41 languages., To appear in ACL 2020

Web search plays an integral role in software engineering (SE) to help with various tasks such as finding documentation, debugging, installation, etc. In this work, we present the first large-scale analysis of web search behavior for SE tasks using the search query logs from Bing, a commercial web search engine. First, we use distant supervision techniques to build a machine learning classifier to extract the SE search queries with an F1 score of 93%. We then perform an analysis on one million search sessions to understand how software engineering related queries and sessions differ from other queries and sessions. Subsequently, we propose a taxonomy of intents to identify the various contexts in which web search is used in software engineering. Lastly, we analyze millions of SE queries to understand the distribution, search metrics and trends across these SE search intents. Our analysis shows that SE related queries form a significant portion of the overall web search traffic. Additionally, we found that there are six major intent categories for which web search is used in software engineering. The techniques and insights can not only help improve existing tools but can also inspire the development of new tools that aid in finding information for SE related tasks.

People can record their pending tasks using to-do lists, digital assistants, and other task management software. In doing so, users of these systems face at least two challenges: (1) they must manually mark their tasks as complete, and (2) when systems proactively remind them about their pending tasks, say, via interruptive notifications, they lack information on task completion status. As a result, people may not realize the full benefits of to-do lists (since these lists can contain both completed and pending tasks) and they may be reminded about tasks they have already done (wasting time and causing frustration). In this paper, we present methods to automatically detect task completion. These inferences can be used to deprecate completed tasks and/or suppress notifications for these tasks (or for other purposes, e.g., task prioritization). Using log data from a popular digital assistant, we analyze temporal dynamics in the completion of tasks and train machine-learned models to detect completion with accuracy exceeding 80% using a variety of features (time elapsed since task creation, task content, email, notifications, user history). The findings have implications for the design of intelligent systems to help people manage their tasks.

Email continues to be one of the most commonly used forms of online communication. As inboxes grow larger, users rely more heavily on email search to effectively find what they are looking for. However, previous studies on email have been exclusive to enterprises with access to large user logs, or limited to small-scale qualitative surveys and analyses on limited public datasets such as Enron1 and Avocado2. In this work, we propose a novel framework that allows for experimentation with real email data. In particular, our approach provides a realistic way of simulating email re-finding tasks in a crowdsourcing environment using the workers' personal email data. We use our approach to experiment with various ranking functions and quality degradation to measure how users behave under different conditions, and conduct analysis across various email types and attributes. Our results show that user behavior can be significantly impacted as a result of the quality of the search ranker, but only when differences in quality are very pronounced. Our analysis confirms that time-based ranking begins to fail as email age increases, suggesting that hybrid approaches may help bridge the gap between relevance-based rankers and the traditional time-based ranking approach. Finally, we also found that users typically reformulate search queries by either entirely re-writing the query, or simply appending terms to the query, which may have implications for email query suggestion facilities.

Despite the centrality of email in the daily routines of knowledge workers, fundamental aspects of its usage are still poorly understood. We are particularly interested in understanding one aspect of email management, email triage, the process of going through unhandled email and deciding what to do with them. In this paper we investigate the email triage behavior by presenting interview and survey results that characterize user behavior and needs. The results highlight current challenges and enhance our understanding of how the triage process can be more effectively supported.

Intelligent assistants can serve many purposes, including entertainment (e.g. playing music), home automation, and task management (e.g. timers, reminders). The role of these assistants is evolving to also support people engaged in work tasks, in workplaces and beyond. To design truly useful intelligent assistants for work, it is important to better understand the work tasks that people are performing. Based on a survey of 401 respondents' daily tasks and activities in a work setting, we present a classification of work-related tasks, and analyze their key characteristics, including the frequency of their self-reported tasks, the environment in which they undertake the tasks, and which, if any, electronic devices are used. We also investigate the cyber, physical, and social aspects of tasks. Finally, we reflect on how intelligent assistants could influence and help people in a work environment to complete their tasks, and synthesize our findings to provide insight on the future of intelligent assistants in support of amplifying personal productivity.

Estimating how long a task will take to complete (i.e., the task duration) is important for many applications, including calendaring and project management. Population-scale calendar data contains distributional information about time allocated by individuals for tasks that may be useful to build computational models for task duration estimation. This study analyzes anonymized large-scale calendar appointment data from hundreds of thousands of individuals and millions of tasks to understand expected task durations and the longitudinal evolution in these durations. Machine-learned models are trained using the appointment data to estimate task duration. Study findings show that task attributes, including content (anonymized appointment subjects), context, and history, are correlated with time allocated for tasks. We also show that machine-learned models can be trained to estimate task duration, with multiclass classification accuracies of almost 80%. The findings have implications for understanding time estimation in populations, and in the design of support in digital assistants and calendaring applications to find time for tasks and to help people, especially those who are new to a task, block sufficient time for task completion.

Email triage involves going through unhandled emails and deciding what to do with them. This familiar process can become increasingly challenging as the number of unhandled email grows. During a triage session, users commonly defer handling emails that they cannot immediately deal with to later. These deferred emails, are often related to tasks that are postponed until the user has more time or the right information to deal with them. In this paper, through qualitative interviews and a large-scale log analysis, we study when and what enterprise email users tend to defer. We found that users are more likely to defer emails when handling them involves replying, reading carefully, or clicking on links and attachments. We also learned that the decision to defer emails depends on many factors such as user's workload and the importance of the sender. Our qualitative results suggested that deferring is very common, and our quantitative log analysis confirms that 12% of triage sessions and 16% of daily active users had at least one deferred email on weekdays. We also discuss several deferral strategies such as marking emails as unread and flagging that are reported by our interviewees, and illustrate how such patterns can be also observed in user logs. Inspired by the characteristics of deferred emails and contextual factors involved in deciding if an email should be deferred, we train a classifier for predicting whether a recently triaged email is actually deferred. Our experimental results suggests that deferral can be classified with modest effectiveness. Overall, our work provides novel insights about how users handle their emails and how deferral can be modeled.

Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset., ACL 2019

The rapidly increasing ubiquity of computing puts a great demand on next-generation human-machine interfaces. Natural language interfaces, exemplified by virtual assistants like Apple Siri and Microsoft Cortana, are widely believed to be a promising direction. However, current natural language interfaces provide users with little help in case of incorrect interpretation of user commands. We hypothesize that the support of fine-grained user interaction can greatly improve the usability of natural language interfaces. In the specific setting of natural language interface to web APIs, we conduct a systematic study to verify our hypothesis. To facilitate this study, we propose a novel modular sequence-to-sequence model to create interactive natural language interfaces. By decomposing the complex prediction process of a typical sequence-to-sequence model into small, highly-specialized prediction units called modules, it becomes straightforward to explain the model prediction to the user, and solicit user feedback to correct possible prediction errors at a fine-grained level. We test our hypothesis by comparing an interactive natural language interface with its non-interactive version through both simulation and human subject experiments with real-world APIs. We show that with the interactive natural language interface, users can achieve a higher success rate and a lower task completion time, which lead to greatly improved user satisfaction.

Email continues to be one of the most important means of online communication, leading to a number of challenges related to information overload and email management. To better understand email management practices in detail, we examine the distribution of visits to emails over time. During their lifetime, emails may be visited one or more times, and with each visit different actions may be taken. Emails that are revisited over time are especially interesting because they represent an opportunity to improve email management and search. In this paper, we present a large-scale log analysis of email revisitation, the activities that people perform on revisited email messages (e.g. responding to, organizing or deleting messages, and opening attachments), and the strategies they use to go back to these emails. We find that most emails have a short lifetime, with more than 33% having a lifetime of less than 5 minutes. We also find that deleting is the most common action taken on messages visited once, and that responding and organizing are more common for messages visited more than once. We complement the log analysis with a survey to understand the motivation behind revisits and the types of emails that are revisited. The survey results show that 73% of the visits are to find information (e.g. a link or document, instructions to perform a task, or answers to questions), while 20% of revisits are to respond to the email. Our findings have implications for designing email clients and intelligent agents that support both short- and long-term revisitation patterns.

User satisfaction metrics are an integral part of search engine development as they help system developers to understand and evaluate the quality of the user experience. Research to date has mostly focused on predicting success or frustration as a proxy for satisfaction. However, users' search experience is more complex than merely being either successful or not. As such, using success rate as a measure of satisfaction can be limiting. In this work, we propose the use of utility as a measure of searcher satisfaction. This concept represents the fulfillment a user receives from con-suming a service and explains how users aim to gain optimal overall satisfaction. Our utility metrics measure the user satisfac-tion by aggregating all their interaction with the search engine. These interactions are represented as a timeline of actions and their dwelltimes, where each action is classified as having a posi-tive or negative effect on the user. We examine sessions mined from Bing logs, with multi-point scale assessment of searcher satisfaction and show that utility is a better proxy for satisfaction compared to success. Leveraging that data, we design metrics of searcher satisfaction that assess the overall utility accumulated by a user during her search session. We use real user traffic from millions of users in an A/B setting to compare utility metrics to success rate metrics. We show that utility is a better metric for evaluating searcher satisfaction with the search engine, and a more sensitive and accurate metric when compared to predicting success. These metrics are currently adopted as the top-level met-ric for evaluating the thousands of A/B experiments that are run on Bing each year.

Detecting and understanding implicit signals of user satisfaction are essential for experimentation aimed at predicting searcher satisfaction. As retrieval systems have advanced, search tasks have steadily emerged as accurate units not only to capture searcher's goals but also in understanding how well a system is able to help the user achieve that goal. However, a major portion of existing work on modeling searcher satisfaction has focused on query level satisfaction. The few existing approaches for task satisfaction prediction have narrowly focused on simple tasks aimed at solving atomic information needs. In this work we go beyond such atomic tasks and consider the problem of predicting user's satisfaction when engaged in complex search tasks composed of many different queries and subtasks. We begin by considering holistic view of user interactions with the search engine result page (SERP) and extract detailed interaction sequences of their activity. We then look at query level abstraction and propose a novel deep sequential architecture which leverages the extracted interaction sequences to predict query level satisfaction. Further, we enrich this model with auxiliary features which have been traditionally used for satisfaction prediction and propose a unified multi-view model which combines the benefit of user interaction sequences with auxiliary features. Finally, we go beyond query level abstraction and consider query sequences issued by the user in order to complete a complex task, to make task level satisfaction predictions. We propose a number of functional composition techniques which take into account query level satisfaction estimates along with the query sequence to predict task level satisfaction. Through rigorous experiments, we demonstrate that the proposed deep sequential models significantly outperform established baselines at both query and task satisfaction prediction. Our findings have implications on metric development for gauging user satisfaction and on designing systems which help users accomplish complex search tasks.

As the Web evolves towards a service-oriented architecture, application program interfaces (APIs) are becoming an increasingly important way to provide access to data, services, and devices. We study the problem of natural language interface to APIs (NL2APIs), with a focus on web APIs for web services. Such NL2APIs have many potential benefits, for example, facilitating the integration of web services into virtual assistants. We propose the first end-to-end framework to build an NL2API for a given web API. A key challenge is to collect training data, i.e., NL command-API call pairs, from which an NL2API can learn the semantic mapping from ambiguous, informal NL commands to formal API calls. We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands. We optimize the crowdsourcing process to further reduce the cost. More specifically, we propose a novel hierarchical probabilistic model for the crowdsourcing process, which guides us to allocate budget to those API calls that have a high value for training NL2APIs. We apply our framework to real-world APIs, and show that it can collect high-quality training data at a low cost, and build NL2APIs with good performance from scratch. We also show that our modeling of the crowdsourcing process can improve its effectiveness, such that the training data collected via our approach leads to better performance of NL2APIs than a strong baseline.

Detecting and understanding implicit measures of user satisfaction are essential for meaningful experimentation aimed at enhancing web search quality. While most existing studies on satisfaction prediction rely on users' click activity and query reformulation behavior, often such signals are not available for all search sessions and as a result, not useful in predicting satisfaction. On the other hand, user interaction data (such as mouse cursor movement) is far richer than just click data and can provide useful signals for predicting user satisfaction. In this work, we focus on considering holistic view of user interaction with the search engine result page (SERP) and construct detailed universal interaction sequences of their activity. We propose novel ways of leveraging the universal interaction sequences to automatically extract informative, interpretable subsequences. In addition to extracting frequent, discriminatory and interleaved subsequences, we propose a Hawkes process model to incorporate temporal aspects of user interaction. Through extensive experimentation we show that encoding the extracted subsequences as features enables us to achieve significant improvements in predicting user satisfaction. We additionally present an analysis of the correlation between various subsequences and user satisfaction. Finally, we demonstrate the usefulness of the proposed approach in covering abandonment cases. Our findings provide a valuable tool for fine-grained analysis of user interaction behavior for metric development.

Email is still among the most popular online activities. People spend a significant amount of time sending, reading and responding to email in order to communicate with others, manage tasks and archive personal information. Most previous research on email is based on either relatively small data samples from user surveys and interviews, or on consumer email accounts such as those from Yahoo! Mail or Gmail. Much less has been published on how people interact with enterprise email even though it contains less automatically generated commercial email and involves more organizational behavior than is evident in personal accounts. In this paper, we extend previous work on predicting email reply behavior by looking at enterprise settings and considering more than dyadic communications. We characterize the influence of various factors such as email content and metadata, historical interaction features and temporal features on email reply behavior. We also develop models to predict whether a recipient will reply to an email and how long it will take to do so. Experiments with the publicly-available Avocado email collection show that our methods outperform all baselines with large gains. We also analyze the importance of different features on reply behavior predictions. Our findings provide new insights about how people interact with enterprise email and have implications for the design of the next generation of email clients.

Email has been central to online communication for the past two decades. Through constant use, new information flows are being defined around users' interactions with emails. Alongside traditional messages, the email inbox is an always-available repository of to-do lists, reminders, files and notes. In this paper, we investigate the use of self-addressed emails (self-Es) as an information management tool, by analysing both: (i) responses to a survey about email use; and (ii) a collection of user donated self-addressed emails. Our results show that sending self-Es is a frequent behaviour among the users we questioned. In addition, we find that to-dos and reminders are the most popular type of information contained in emails-to-self. Our findings have direct implications for the development of systems that support novel interactions with information flows centred around email.

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.

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.

We introduce a latent activity model for workplace emails, positing that communication at work is purposeful and organized by activities. We pose the problem as probabilistic inference in graphical models that jointly capture the interplay between latent activities and the email contexts they govern, such as the recipients, subject and body. The model parameters are learned using maximum likelihood estimation with an expectation maximization algorithm. We present three variants of the model that incorporate the recipients, co-occurrence of the recipients, and email body and subject. We demonstrate the model’s effectiveness in an email recipient recommendation task and show that it outperforms a state-of-the-art generative model. Additionally, we show that the activity model can be used to identify email senders who engage in similar activities, resulting in further improvements in recipient recommendation.

Voice interactions are becoming more prevalent as the usage of voice search and intelligent assistants gains more popularity. Users frequently reformulate their requests in hope of getting better results either because the system was unable to recognize what they said or because it was able to recognize it but was unable to return the desired response. Query reformulation has been extensively studied in the context of text input. Many of the characteristics studied in the context of text query reformulation are potentially useful for voice query reformulation. However, voice query reformulation has its unique characteristics in terms of the reasons that lead users to reformulating their queries and how they reformulate them. In this paper, we study the problem of voice query reformulation. We perform a large scale human annotation study to collect thousands of labeled instances of voice reformulation and non-reformulation query pairs. We use this data to compare and contrast characteristics of reformulation and non-reformulation queries over a large a number of dimensions. We then train classifiers to distinguish between reformulation and non-reformulation query pairs and to predict the rationale behind reformulation. We demonstrate through experiments with the human labeled data that our classifiers achieve good performance in both tasks.

Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is challenging to evaluate them due to the varied and evolving number of tasks supported, e.g., voice command, web search, and chat. Since each task may have its own procedure and a unique form of correct answers, it is expensive to evaluate each task individually. This paper is the first attempt to solve this challenge. We develop consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. We use implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and intent classification. Using this approach, we can potentially evaluate and compare different tasks within and across intelligent assistants ac-cording to the predicted user satisfaction rates. Our approach is characterized by an automatic scheme of categorizing user-system interaction into task-independent dialog actions, e.g., the user is commanding, selecting, or confirming an action. We use the action sequence in a session to predict user satisfaction and the quality of speech recognition and intent classification. We also incorporate other features to further improve our approach, including features derived from previous work on web search satisfaction prediction, and those utilizing acoustic characteristics of voice requests. We evaluate our approach using data collected from a user study. Results show our approach can accurately identify satisfactory and unsatisfactory sessions.

We present methods to automatically identify and recommend sub-tasks to help people explore and accomplish complex search tasks. Although Web searchers often exhibit directed search behaviors such as navigating to a particular Website or locating a particular item of information, many search scenarios involve more complex tasks such as learning about a new topic or planning a vacation. These tasks often involve multiple search queries and can span multiple sessions. Current search systems do not provide adequate support for tackling these tasks. Instead, they place most of the burden on the searcher for discovering which aspects of the task they should explore. Particularly challenging is the case when a searcher lacks the task knowledge necessary to decide which step to tackle next. In this paper, we propose methods to automatically mine search logs for tasks and build an association graph connecting multiple tasks together. We then leverage the task graph to assist new searchers in exploring new search topics or tackling multi-step search tasks. We demonstrate through experiments with human participants that we can discover related and interesting tasks to assist with complex search scenarios.