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Cheng Li

Cheng Li

I got my Ph.D. in information science from the University of Michigan, Ann Arbor. My advisor is Qiaozhu Mei. I received my Bachelor's degree from the College of Computer Science and Technology at Zhejiang University, China. I am particularly interested in data mining, information retrieval, machine learning (especially deep learning), and natural language processing, with application to the retrieval, management, and analysis of data from Web, scientific literature, social networks, and various online communities. My publications can be found in Google Scholar
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    Preview abstract Facilitated by large language models (LLMs), personalized text generation has become a rapidly growing research direction. Most existing studies focus on designing specialized models for a particular domain, or they require fine-tuning the LLMs to generate personalized text. We consider a typical scenario in which the large language model, which generates personalized output, is frozen and can only be accessed through APIs. Under this constraint, all one can do is to improve the input text (i.e., text prompts) sent to the LLM, a procedure that is usually done manually. In this paper, we propose a novel method to automatically revise prompts for personalized text generation. The proposed method takes the initial prompts generated by a state-of-the-art, multistage framework for personalized generation and rewrites a few critical components that summarize and synthesize the personal context. The prompt rewriter employs a training paradigm that chains together supervised learning (SL) and reinforcement learning (RL), where SL reduces the search space of RL and RL facilitates end-to-end training of the rewriter. Using datasets from three representative domains, we demonstrate that the rewritten prompts outperform both the original prompts and the prompts optimized via supervised learning or reinforcement learning alone. In-depth analysis of the rewritten prompts shows that they are not only human readable, but also able to guide manual revision of prompts when there is limited resource to employ reinforcement learning to train the prompt rewriter, or when it is costly to deploy an automatic prompt rewriter for inference. View details
    End-to-End Query Term Weighting
    Karan Samel
    Swaraj Khadanga
    Wensong Xu
    Xingyu Wang
    Kashyap Kolipaka
    Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '23) (2023)
    Preview abstract Bag-of-words based lexical retrieval systems are still the most commonly used methods for real-world search applications. Recently deep learning methods have shown promising results to improve this retrieval performance but are expensive to run in an online fashion, non-trivial to integrate into existing production systems, and might not generalize well in out-of-domain retrieval scenarios. Instead, we build on top of lexical retrievers by proposing a Term Weighting BERT (TW-BERT) model. TW-BERT learns to predict the weight for individual n-gram (e.g., uni-grams and bi-grams) query input terms. These inferred weights and terms can be used directly by a retrieval system to perform a query search. To optimize these term weights, TW-BERT incorporates the scoring function used by the search engine, such as BM25, to score query-document pairs. Given sample query-document pairs we can compute a ranking loss over these matching scores, optimizing the learned query term weights in an end-to-end fashion. Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing production applications, whereas existing deep learning based search methods would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion. We show that TW-BERT improves retrieval performance over strong term weighting baselines within MSMARCO and in out-of-domain retrieval on TREC datasets. View details
    Job Type Extraction for Service Businesses
    Yaping Qi
    Hayk Zakaryan
    Yonghua Wu
    Companion Proceedings of the ACM Web Conference 2023
    Preview abstract Google My Business (GMB) is a platform that allows business owners to manage their business profiles, which will be displayed when a user issues a relevant query on Google Search or Maps. Many GMB businesses provide diverse services from home cleaning and plumbing to legal services and education. However the exact service content, which we call job types, is often missing in their profiles. This leaves the burden of finding such content to the users, either by the tedious work of scanning through business websites or time-consuming calling of the owners. In the present paper, we describe how we build a pipeline to automatically extract the job types from websites of business owners and how we solve scalability issues for deployment. Rather than focusing on developing novel and sophisticated machine learning models, we share various challenges we have faced and practical experiences of building such a pipeline, including the cold start problem of dataset collection with limited human annotation resource, scalability, reaching a launch bar of high precision, and building a general pipeline with reasonable coverage of any free-text web pages without relying on the Document Object Model (DOM) structure. With these challenges, standard approaches for information extraction do not directly apply or are not scalable to be served. In this paper, we show how we address these challenges in different stages of the extraction pipeline, including: (1) utilizing structured content like tables and lists to tackle the cold start problem of dataset collection; (2) exploitation of various context information to improve model performance without hurting scalability; and (3) formulating the extraction problem as a retrieval task to improve generalizability, efficiency as well as coverage. The pipeline has been successfully deployed, and is scalable enough to be refreshed every few days to extract the latest online information. The extracted job types are serving millions of users of Google Search and Google Maps with at least three use cases: (1) job types of a place are directly displayed on mobile devices; (2) job types provide explanation as to why a place shows up given a query; (3) job types are used as a signal to rank business places. According to a user survey, the displayed job types has greatly enhanced the probability of a user hiring a service provider. View details
    Learning Sparse Lexical Representations Over Expanded Vocabularies for Retrieval
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23) (2023)
    Preview abstract A recent line of work in first-stage Neural Information Retrieval has focused on learning sparse lexical representations instead of dense embeddings. One such work is SPLADE, which has been shown to lead to state-of-the-art results in both the in-domain and zero-shot settings, can leverage inverted indices for efficient retrieval, and offers enhanced interpretability. However, existing SPLADE models are fundamentally limited to learning a sparse representation based on the native BERT WordPiece vocabulary. In this work, we extend SPLADE to support learning sparse representations over arbitrary sets of tokens to improve flexibility and aid integration with existing retrieval systems. As an illustrative example, we focus on learning a sparse representation over a large (300k) set of unigrams. We add an unsupervised pretraining task on C4 to learn internal representations for new tokens. Our experiments show that our Expanded-SPLADE model maintains the performance of WordPiece-SPLADE on both in-domain and zero-shot retrieval while allowing for custom output vocabularies. View details
    SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval
    Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), ACM (2023) (to appear)
    Preview abstract In dense retrieval, prior work has largely improved retrieval effectiveness using multi-vector dense representations, exemplified by ColBERT. In sparse retrieval, more recent work, such as SPLADE, demonstrated that one can also learn sparse lexical representations to achieve comparable effectiveness while enjoying better interpretability. In this work, we combine the strengths of both the sparse and dense representations for first-stage retrieval. Specifically, we propose SparseEmbed – a novel retrieval model that learns sparse lexical representations with contextual embeddings. Compared with SPLADE, our model leverages the contextual embeddings to improve model expressiveness. Compared with ColBERT, our sparse representations are trained end-to-end to optimize both efficiency and effectiveness. View details
    Multi-Aspect Dense Retrieval
    Swaraj Khadanga
    Wensong Xu
    Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM (2022)
    Preview abstract Prior work in Dense Retrieval usually encodes queries and documents using single-vector representations (also called embeddings) and performs retrieval in the embedding space using approximate nearest neighbor search. This paradigm enables efficient semantic retrieval. However, the single-vector representations can be ineffective at capturing different aspects of the queries and documents in relevance matching, especially for some vertical domains. For example, in e-commerce search, these aspects could be category, brand and color. Given a query "white nike socks", a Dense Retrieval model may mistakenly retrieve some "white adidas socks" while missing out the intended brand. We propose to explicitly represent multiple aspects using one embedding per aspect. We introduce an aspect prediction task to teach the model to capture aspect information with particular aspect embeddings. We design a lightweight network to fuse the aspect embeddings for representing queries and documents. Our evaluation using an e-commerce dataset shows impressive improvements over strong Dense Retrieval baselines. We also discover that the proposed aspect embeddings can enhance the interpretability of Dense Retrieval models as a byproduct. View details
    Preview abstract Document layout comprises both structural and visual (\eg font size) information that are vital but often ignored by machine learning models. The few existing models which do use layout information only consider \textit{textual} contents, and overlook the existence of contents in other modalities such as images. Additionally, spatial interactions of presented contents in a layout was never fully exploited. On the other hand, a series of document understanding tasks are calling out for layout information. One example is given a position in a document, which image is the best to fit in. To address current models' limitations and tackle layout-aware document understanding tasks, we first parse a document into blocks whose content can be textual, tabular, or multimedia (\eg images) using a proprietary tool. We then propose a novel hierarchical framework, LAMPreT, to encode the blocks. Our LAMPreT model encodes each block with a multimodal transformer in the lower-level, and aggregates the block-level representations and connections utilizing a specifically designed transformer at the higher-level. We design hierarchical pre-training objectives where the lower-level model is trained with the standard masked language modeling (MLM) loss and the multimodal alignment loss, and the higher-level model is trained with three layout-aware objectives: (1) block-order predictions, (2) masked block predictions, and (3) image fitting predictions. We test the proposed model on two layout-aware tasks -- image suggestions and text block filling, and show the effectiveness of our proposed hierarchical architecture as well as pre-training techniques. View details
    Preview abstract Search engines often follow a 2-phase paradigm where in the first step an initial set of documents is retrieved (the \emp{retrieval} step) and in the second step the documents are ranked so as to obtain the final result list (the \emp{re-ranking} step). The focus of this paper is on improving the \emph{retrieval} step (measured mainly by recall) using deep neural network-based approaches. While deep neural networks were shown to improve the performance of the re-ranking step, there is little literature about using deep neural networks to improve the retrieval step. Previous works on deep neural networks for IR usually apply a simple lexical retrieval model for the retrieval step (e.g., BM25) and emphasize on the re-ranking step. In this paper, we propose and study a hybrid retrieval approach, which leverages both semantic (deep neural network based) and lexical (keyword matching based like BM25) matching techniques. The main idea is to perform semantic and lexical retrieval in parallel, and then to combine the result lists to generate the initial result set for re-ranking. An empirical evaluation, using a public TREC collection, shows that semantic retrieval model generated result lists often contain a substantial number of relevant documents not covered by the lexical-based generated lists. Further analysis of these relevant documents shows that they often also exhibit different characteristics than the lexical-based documents, attesting to the complementary nature of the two approaches. Finally, the experiments show that by combining the two result lists, the recall of the result list can increase significantly, the retrieval step can be greatly improved and these improvements are highly robust. View details
    Preview abstract Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on matching between short texts (e.g., question answering), or between a short and a long text (e.g., ad-hoc retrieval). Semantic matching between long-form documents, which has many important applications like news recommendation, related article recommendation and document clustering, is relatively less explored and needs more research effort. In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in the task of text matching. These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length. In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. We propose a transformer based hierarchical encoder to capture the document structure information. In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT. Our experimental results on several benchmark datasets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT. Comparing to BERT based baselines, our model is able to increase maximum input text length from 512 to 2048. We will open source a Wikipedia based benchmark dataset, code and a pre-trained checkpoint to accelerate future research on long-form document matching. View details
    Preview abstract Semantic text matching is one of the most important research problems in many domains, including, but not limited to, information retrieval, question answering, and recommendation. Among the different types of semantic text matching, long-document-to-long-document text matching has many applications, but has rarely been studied. Most existing approaches for semantic text matching have limited success in this setting, due to their inability to capture and distill the main ideas and topics from long-form text. In this paper, we propose a novel Siamese multi-depth attention-based hierarchical recurrent neural network (SMASH RNN) that learns the long-form semantics, and enables long-form document based semantic text matching. In addition to word information, SMASH RNN is using the document structure to improve the representation of long-form documents. Specifically, SMASH RNN synthesizes information from different document structure levels, including paragraphs, sentences, and words. An attention-based hierarchical RNN derives a representation for each document structure level. Then, the representations learned from the different levels are aggregated to learn a more comprehensive semantic representation of the entire document. For semantic text matching, a Siamese structure couples the representations of a pair of documents, and infers a probabilistic score as their similarity. We conduct an extensive empirical evaluation of SMASH RNN with three practical applications, including email attachment suggestion, related article recommendation, and citation recommendation. Experimental results on public data sets demonstrate that SMASH RNN significantly outperforms competitive baseline methods across various classification and ranking scenarios in the context of semantic matching of long-form documents. View details
    Multi-view Embedding-based Synonyms for Personal Search
    Hongbo Deng
    Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19) (2019), pp. 575-584
    Preview abstract Synonym expansion is a technique that adds related words to search queries, which may lead to more relevant documents being retrieved, thus improving recall. There is extensive prior work on synonym expansion for web search, however very few studies have tackled its application for email search. Synonym expansion for private corpora like emails poses several unique research challenges. First, the emails are not shared across users, which precludes us from directly employing query-document bipartite graphs, which are standard in web search synonym expansion. Second, user search queries are of personal nature, and may not be generalizable across users. Third, the size of the underlying corpora from which the synonyms may be mined is relatively small (i.e., user's private email inbox) compared to the size of the web corpus. Therefore, in this paper, we propose a solution tailored to the challenges of synonym expansion for email search. We formulate it as a multi-view learning problem, and propose a novel embedding-based model that joins information from multiple sources to obtain the optimal synonym candidates. To demonstrate the effectiveness of the proposed technique, we evaluate our model using both explicit human ratings as well as a live experiment using the Gmail Search service, one of the world's largest email search engines. View details
    Preview abstract Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse Propensity Scoring (IPS), to learn a ranking function from biased click data. They handle the click incompleteness bias, but usually assume that the clicks are noise-free, i.e., a clicked document is always assumed to be relevant. In this paper, we relax this unrealistic assumption and study click noise explicitly in the unbiased learning-to-rank setting. Specifically, we model the noise as the position-dependent trust bias and propose a noise-aware Position-Based Model, named TrustPBM, to better capture user click behavior. We propose an Expectation-Maximization algorithm to estimate both examination and trust bias from click data in TrustPBM. Furthermore, we show that it is difficult to use a pure IPS method to incorporate click noise and thus propose a novel method that combines a Bayes rule application with IPS for unbiased learning-to-rank. We evaluate our proposed methods on three personal search data sets and demonstrate that our proposed model can significantly outperform the existing unbiased learning-to-rank methods. View details
    TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
    Sebastian Bruch
    Rohan Anil
    Stephan Wolf
    Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2019), pp. 2970-2978
    Preview abstract Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact in metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank. View details
    Estimating Position Bias without Intrusive Interventions
    Aman Agarwal
    Ivan Zaitsev
    Thorsten Joachims
    Proceedings of the 12 ACM International Conference on Web Search and Data Mining (2019), pp. 474-482
    Preview abstract Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies. View details
    Preview abstract Ranking functions are used to return ranked lists of items for users to interact. How to evaluate ranking functions using historical user interaction logs, as known as off-policy evaluation, is an important but challenging problem. The commonly used Inverse Propensity Scores (IPS) approaches works better for the single item case, but suffer from extremely low data efficiency for the ranked list case. In this paper, we study how to improve the data efficiency of IPS approaches in the offline comparison setting. We propose two approaches Trunc-match and Rand-interleaving for offline comparison using uniformly randomized data. We show that these methods can improve the data efficiency and also the comparison sensitivity based on one of the largest email search engines. View details
    The LambdaLoss Framework for Ranking Metric Optimization
    Proceedings of The 27th ACM International Conference on Information and Knowledge Management (CIKM '18), ACM (2018), pp. 1313-1322
    Preview abstract How to optimize ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) is an important but challenging problem, because ranking metrics are either flat or discontinuous everywhere, which makes them hard to be optimized directly. Among existing approaches, LambdaRank is a novel algorithm that incorporates ranking metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, the underlying loss that LambdaRank optimizes for remains unknown until now. Due to this, there is no principled way to advance the LambdaRank algorithm further. In this paper, we present LambdaLoss, a probabilistic framework for ranking metric optimization. We show that LambdaRank is a special configuration with a well-defined loss in the LambdaLoss framework, and thus provide theoretical justification for it. More importantly, the LambdaLoss framework allows us to define metric-driven loss functions that have clear connection to different ranking metrics. We show a few cases in this paper and evaluate them on three publicly available data sets. Experimental results show that our metric-driven loss functions can significantly improve the state-of-the-art learning-to-rank algorithms. View details
    Preview abstract TensorFlow Ranking is the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. View details
    Related Event Discovery
    Sujith Ravi
    Vijay Garg
    Proceedings of WSDM (2017)
    Preview abstract In this paper, we consider the problem of discovering local events on the web, where events are entities extracted from pages with Schema.org annotations. Examples of such local events include small venue concerts, farmers markets, sports activities, etc. Given an event entity, we propose a graph-based framework for retrieving a ranked list of related events that a user is likely to be interested in or to attend. We demonstrate that this framework can be easily extended to the keyword search scenario as well, where the user issues a query to a search engine, hoping to find relevant events to attend. Due to the difficulty of obtaining ground-truth labels for event entities, which are temporal and are constrained by location, our general retrieval framework is unsupervised, and its graph-based formulation addresses (a) the challenge of feature sparseness and noisiness, and (b) semantic mismatch problem in a self-contained and principled manner. To validate our methods, we collect human annotations and conduct a comprehensive empirical study, analyzing the performance of our methods with regard to relevance, recall, and diversity. This study shows that our graph-based framework is significantly better than any individual feature for both entity and keyword search scenarios, and can be further improved with minimal supervision. Finally, we demonstrate that our framework can be useful in understanding the temporal and the localized nature of the events on the web. View details
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