arXiv subjects

Eugene Agichtein

Publications and source records attributed to Eugene Agichtein.

At least 37 records · Page 2Linked to original sources

arXiv2022

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built on physical measurement data collected from sensors, they may not be suitable for predicting public health effects experienced from pollution exposure. This study aims to develop and validate models to nowcast the observed pollution levels using Web search data, which is publicly available in near real-time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level, by using generally available meteorological data and aggregate Web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting three critical air pollutants (ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)), across ten major U.S. metropolitan statistical areas (MSAs) in 2017 and 2018.

cs.LG↗

arXiv2022

Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance

Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge. The goal of the challenge is to build agents capable of conversing coherently and engagingly with humans on popular topics for 20 minutes, while achieving an average rating of at least 4.0/5.0. However, as conversational agents attempt to assist users with increasingly complex tasks, new conversational AI techniques and evaluation platforms are needed. The Alexa Prize TaskBot challenge, established in 2021, builds on the success of the SocialBot challenge by introducing the requirements of interactively assisting humans with real-world Cooking and Do-It-Yourself tasks, while making use of both voice and visual modalities. This challenge requires the TaskBots to identify and understand the user's need, identify and integrate task and domain knowledge into the interaction, and develop new ways of engaging the user without distracting them from the task at hand, among other challenges. This paper provides an overview of the TaskBot challenge, describes the infrastructure support provided to the teams with the CoBot Toolkit, and summarizes the approaches the participating teams took to overcome the research challenges. Finally, it analyzes the performance of the competing TaskBots during the first year of the competition.

cs.CL↗

arXiv2021

Identifying Helpful Sentences in Product Reviews

In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.

cs.CL↗

arXiv2021

Diversifying Multi-aspect Search Results Using Simpson's Diversity Index

In search and recommendation, diversifying the multi-aspect search results could help with reducing redundancy, and promoting results that might not be shown otherwise. Many previous methods have been proposed for this task. However, previous methods do not explicitly consider the uniformity of the number of the items' classes, or evenness, which could degrade the search and recommendation quality. To address this problem, we introduce a novel method by adapting the Simpson's Diversity Index from biology, which enables a more effective and efficient quadratic search result diversification algorithm. We also extend the method to balance the diversity between multiple aspects through weighted factors and further improve computational complexity by developing a fast approximation algorithm. We demonstrate the feasibility of the proposed method using the openly available Kaggle shoes competition dataset. Our experimental results show that our approach outperforms previous state of the art diversification methods, while reducing computational complexity.

cs.IR↗

arXiv2021

RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search

To support complex search tasks, where the initial information requirements are complex or may change during the search, a search engine must adapt the information delivery as the user's information requirements evolve. To support this dynamic ranking paradigm effectively, search result ranking must incorporate both the user feedback received, and the information displayed so far. To address this problem, we introduce a novel reinforcement learning-based approach, RLIrank. We first build an adapted reinforcement learning framework to integrate the key components of the dynamic search. Then, we implement a new Learning to Rank (LTR) model for each iteration of the dynamic search, using a recurrent Long Short Term Memory neural network (LSTM), which estimates the gain for each next result, learning from each previously ranked document. To incorporate the user's feedback, we develop a word-embedding variation of the classic Rocchio Algorithm, to help guide the ranking towards the high-value documents. Those innovations enable RLIrank to outperform the previously reported methods from the TREC Dynamic Domain Tracks 2017 and exceed all the methods in 2016 TREC Dynamic Domain after multiple search iterations, advancing the state of the art for dynamic search.

cs.IR↗

arXiv2021

De-Biased Modelling of Search Click Behavior with Reinforcement Learning

Users' clicks on Web search results are one of the key signals for evaluating and improving web search quality and have been widely used as part of current state-of-the-art Learning-To-Rank(LTR) models. With a large volume of search logs available for major search engines, effective models of searcher click behavior have emerged to evaluate and train LTR models. However, when modeling the users' click behavior, considering the bias of the behavior is imperative. In particular, when a search result is not clicked, it is not necessarily chosen as not relevant by the user, but instead could have been simply missed, especially for lower-ranked results. These kinds of biases in the click log data can be incorporated into the click models, propagating the errors to the resulting LTR ranking models or evaluation metrics. In this paper, we propose the De-biased Reinforcement Learning Click model (DRLC). The DRLC model relaxes previously made assumptions about the users' examination behavior and resulting latent states. To implement the DRLC model, convolutional neural networks are used as the value networks for reinforcement learning, trained to learn a policy to reduce bias in the click logs. To demonstrate the effectiveness of the DRLC model, we first compare performance with the previous state-of-art approaches using established click prediction metrics, including log-likelihood and perplexity. We further show that DRLC also leads to improvements in ranking performance. Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.

cs.IR↗

arXiv2021

APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization

Query categorization is an essential part of query intent understanding in e-commerce search. A common query categorization task is to select the relevant fine-grained product categories in a product taxonomy. For frequent queries, rich customer behavior (e.g., click-through data) can be used to infer the relevant product categories. However, for more rare queries, which cover a large volume of search traffic, relying solely on customer behavior may not suffice due to the lack of this signal. To improve categorization of rare queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the latent knowledge embedded in semantically or lexically similar product documents to enrich the representation of the more rare queries. To this end, we propose a novel deep neural model named Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization. To demonstrate the effectiveness of our approach, we collect search queries from a large commercial search engine, and compare APRF-Net to state-of-the-art deep learning models for text classification. Our results show that the APRF-Net significantly improves query categorization by 5.9% on F1@1 score over the baselines, which increases to 8.2% improvement for the rare (tail) queries. The findings of this paper can be leveraged for further improvements in search query representation and understanding.

cs.IR↗

arXiv2021

DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search

Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e.g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding. To address these problems, we propose a deep learning model, DeepCAT, which learns joint word-category representations to enhance the query understanding process. We believe learning category interactions helps to improve the performance of category mapping on minority classes, tail and torso queries. DeepCAT contains a novel word-category representation model that trains the category representations based on word-category co-occurrences in the training set. The category representation is then leveraged to introduce a new loss function to estimate the category-category co-occurrences for refining joint word-category embeddings. To demonstrate our model's effectiveness on minority categories and tail queries, we conduct two sets of experiments. The results show that DeepCAT reaches a 10% improvement on minority classes and a 7.1% improvement on tail queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.

cs.IR↗

arXiv2020

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce. Compared to web documents, product catalogs are more structured and sparse due to multi-instance fields that encode heterogeneous aspects of products (e.g. brand name and product dimensions). In this paper, we propose a new semantic product search algorithm that learns to represent and aggregate multi-instance fields into a document representation using state of the art transformers as encoders. Our experiments investigate two aspects of the proposed approach: (1) effectiveness of field representations and structured matching; (2) effectiveness of adding lexical features to semantic search. After training our models using user click logs from a well-known E-commerce platform, we show that our results provide useful insights for improving product search. Lastly, we present a detailed error analysis to show which types of queries benefited the most by fielded representations and structured matching.

cs.IR↗

arXiv2020

Quantifying the Effects of Prosody Modulation on User Engagement and Satisfaction in Conversational Systems

As voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems.

cs.HC↗

arXiv2020

Would You Like to Hear the News? Investigating Voice-BasedSuggestions for Conversational News Recommendation

One of the key benefits of voice-based personal assistants is the potential to proactively recommend relevant and interesting information. One of the most valuable sources of such information is the News. However, in order for the user to hear the news that is useful and relevant to them, it must be recommended in an interesting and informative way. However, to the best of our knowledge, how to present a news item for a voice-based recommendation remains an open question. In this paper, we empirically compare different ways of recommending news, or specific news items, in a voice-based conversational setting. Specifically, we study the user engagement and satisfaction with five different variants of presenting news recommendations: (1) a generic news briefing; (2) news about a specific entity relevant to the current conversation; (3) news about an entity from a past conversation; (4) news on a trending news topic; and (5) the default - a suggestion to talk about news in general. Our results show that entity-based news recommendations exhibit 29% higher acceptance compared to briefing recommendations, and almost 100% higher acceptance compared to recommending generic or trending news. Our investigation into the presentation of news recommendations and the resulting insights could make voice assistants more informative and engaging.

cs.IR↗

arXiv2020

Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.

cs.HC↗

arXiv2020

JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.

cs.IR↗

arXiv2020

ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents

Identifying the topic (domain) of each user's utterance in open-domain conversational systems is a crucial step for all subsequent language understanding and response tasks. In particular, for complex domains, an utterance is often routed to a single component responsible for that domain. Thus, correctly mapping a user utterance to the right domain is critical. To address this problem, we introduce ConCET: a Concurrent Entity-aware conversational Topic classifier, which incorporates entity-type information together with the utterance content features. Specifically, ConCET utilizes entity information to enrich the utterance representation, combining character, word, and entity-type embeddings into a single representation. However, for rich domains with millions of available entities, unrealistic amounts of labeled training data would be required. To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases to generate additional labeled utterances. We extensively evaluate ConCET and our proposed training method first on an openly available human-human conversational dataset called Self-Dialogue, to calibrate our approach against previous state-of-the-art methods; second, we evaluate ConCET on a large dataset of human-machine conversations with real users, collected as part of the Amazon Alexa Prize. Our results show that ConCET significantly improves topic classification performance on both datasets, including 8-10% improvements over state-of-the-art deep learning methods. We complement our quantitative results with detailed analysis of system performance, which could be used for further improvements of conversational agents.

cs.CL↗

arXiv2020

Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents

To hold a true conversation, an intelligent agent should be able to occasionally take initiative and recommend the next natural conversation topic. This is a challenging task. A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it. Thus, a scripted, or one-size-fits-all, popularity-based topic suggestion is doomed to fail. Instead, we explore different methods for a personalized, contextual topic suggestion for open-domain conversations. We formalize the Conversational Topic Suggestion problem (CTS) to more clearly identify the assumptions and requirements. We also explore three possible approaches to solve this problem: (1) model-based sequential topic suggestion to capture the conversation context (CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previous successful conversations from similar users (CTS-CF), and (3) a hybrid approach combining both conversation context and collaborative filtering. To evaluate the effectiveness of these methods, we use real conversations collected as part of the Amazon Alexa Prize 2018 Conversational AI challenge. The results are promising: the CTS-Seq model suggests topics with 23% higher accuracy than the baseline, and incorporating collaborative filtering signals into a hybrid CTS-Seq-CF model further improves recommendation accuracy by 12%. Together, our proposed models, experiments, and analysis significantly advance the study of open-domain conversational agents, and suggest promising directions for future improvements.

cs.CL↗

arXiv2020

Contextual Dialogue Act Classification for Open-Domain Conversational Agents

Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational agents. While DA classification has been extensively studied in human-human conversations, it has not been sufficiently explored for the emerging open-domain automated conversational agents. Moreover, despite significant advances in utterance-level DA classification, full understanding of dialogue utterances requires conversational context. Another challenge is the lack of available labeled data for open-domain human-machine conversations. To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification. Specifically, we use transfer learning to adapt models trained on human-human conversations to predict dialogue acts in human-machine dialogues. To investigate the effectiveness of our method, we train our model on the well-known Switchboard human-human dialogue dataset, and fine-tune it for predicting dialogue acts in human-machine conversation data, collected as part of the Amazon Alexa Prize 2018 competition. The results show that the CDAC model outperforms an utterance-level state of the art baseline by 8.0% on the Switchboard dataset, and is comparable to the latest reported state-of-the-art contextual DA classification results. Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.

cs.CL↗

arXiv2020

Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media

Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1. Decomposing the task into domain-specific sub-tasks by identifying key concepts, thus utilizing human domain understanding; and 2. Combining the results of learners for each key concept using co-training to reduce the requirements for labeled training data. We empirically show the effectiveness and generality of our approach, Co-Decomp, using three representative social media mining tasks, namely Personal Health Mention detection, Crisis Report detection, and Adverse Drug Reaction monitoring. The experiments show that our model is able to outperform the state-of-the-art text classification models--including those using the recently introduced BERT model--when small amounts of training data are available.

cs.CL↗

arXiv2018

Learning to Focus when Ranking Answers

One of the main challenges in ranking is embedding the query and document pairs into a joint feature space, which can then be fed to a learning-to-rank algorithm. To achieve this representation, the conventional state of the art approaches perform extensive feature engineering that encode the similarity of the query-answer pair. Recently, deep-learning solutions have shown that it is possible to achieve comparable performance, in some settings, by learning the similarity representation directly from data. Unfortunately, previous models perform poorly on longer texts, or on texts with significant portion of irrelevant information, or which are grammatically incorrect. To overcome these limitations, we propose a novel ranking algorithm for question answering, QARAT, which uses an attention mechanism to learn on which words and phrases to focus when building the mutual representation. We demonstrate superior ranking performance on several real-world question-answer ranking datasets, and provide visualization of the attention mechanism to otter more insights into how our models of attention could benefit ranking for difficult question answering challenges.

cs.CL↗