The task of Visual Text-to-Speech (VisualTTS), also known as video dubbing, aims to generate speech synchronized with the lip movements in an input video, in additional to being consistent with the content of input text and cloning the timbre of a reference speech. Existing VisualTTS models typically adopt lightweight architectures and design specialized modules to achieve the above goals respectively, yet the speech quality is not satisfied due to the model capacity and the limited data in VisualTTS. Recently, speech large language models (SpeechLLM) show the robust ability to generate high-quality speech. But few work has been done to well leverage temporal cues from video input in generating lip-synchronized speech. To generate both high-quality and lip-synchronized speech in VisualTTS tasks, we propose a novel Visual Speech Language Model called VSpeechLM based upon a SpeechLLM. To capture the synchronization relationship between text and video, we propose a text-video aligner. It first learns fine-grained alignment between phonemes and lip movements, and then outputs an expanded phoneme sequence containing lip-synchronization cues. Next, our proposed SpeechLLM based decoders take the expanded phoneme sequence as input and learns to generate lip-synchronized speech. Extensive experiments demonstrate that our VSpeechLM significantly outperforms previous VisualTTS methods in terms of overall quality, speaker similarity, and synchronization metrics.

Simulating high quality user behavior data has always been a fundamental yet challenging problem in human-centered applications such as recommendation systems, social networks, among many others. The major difficulty of user behavior simulation originates from the intricate mechanism of human cognitive and decision processes. Recently, substantial evidence has suggested that by learning huge amounts of web knowledge, large language models (LLMs) can achieve human-like intelligence and generalization capabilities. Inspired by such capabilities, in this article, we take an initial step to study the potential of using LLMs for user behavior simulation in the recommendation domain. To make LLMs act like humans, we design profile, memory and action modules to equip them, building LLM-based agents to simulate real users. To enable interactions between different agents and observe their behavior patterns, we design a sandbox environment, where each agent can interact with the recommendation system, and different agents can converse with their friends via one-to-one chatting or one-to-many social broadcasting. In the experiments, we first demonstrate the believability of the agent-generated behaviors based on both subjective and objective evaluations. Then, to show the potential applications of our method, we simulate and study two social phenomena including (1) information cocoons and (2) user conformity behaviors. We find that controlling the personalization degree of recommendation algorithms and improving the heterogeneity of user social relations can be two effective strategies for alleviating the problem of information cocoon, and the conformity behaviors can be highly influenced by the amount of user social relations. To advance this direction, we have released our project at https://github.com/RUC-GSAI/YuLan-Rec .

While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions.

While previous video to text models have achieved remarkable successes, they mostly focus on how to understand the video contents in a general sense, but fail to capture the human personalized preferences, which is highly demanded for an engaging multimodal chatbots. Different from user modeling in collaborative filtering, there is no other user behaviors in inference as a real-time video stream is coming. In this paper, we formally define the task of personalized video commenting task and design an end-to-end personalized framework for solving this task. In specific, we argue that the personalization for video comment generation can be reflected in two aspects, that is, (1) for the same video, different users may comment on different clips, and (2) for the same clip, different people may also express various opinions with diverse commentary styles. Motivated by these considerations, we design our framework based on two components. The first one is a clip selector, which is responsible for predicting the clips that the user may comment in the video. The second one is a text generator, which aims to produce the comment based on the above predicted clips and the user's preference. In our framework, these two components are optimized in an end-to-end manner to mutually enhance each other, where we design confidence-aware scheduled sampling and iterative inference strategies to solve the problem that the ground truth clips are absent in the inference phase. As the absence of personalized video to text dataset, we collect and release a new dataset for studying this problem. We conduct extensive experiments to demonstrate the effectiveness of our model.

Generating a text based on a predefined guideline is an interesting but challenging problem. A series of studies have been carried out in recent years. In dialogue systems, researchers have explored driving a dialogue based on a plan, while in story generation, a storyline has also been proved to be useful. In this article, we address a new task—generating movie scripts based on a predefined narrative. As an early exploration, we study this problem in a “retrieval-based” setting. We propose a model (ScriptWriter-CPre) to select the best response (i.e., next script line) among the candidates that fit the context (i.e., previous script lines) as well as the given narrative. Our model can keep track of what in the narrative has been said and what is to be said. Besides, it can also predict which part of the narrative should be paid more attention to when selecting the next line of script. In our study, we find the narrative plays a different role than the context. Therefore, different mechanisms are designed for deal with them. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end-users can upload their narratives freely when watching a movie. This new dataset is made available publicly to facilitate other studies in text generation under the guideline. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.

For social bots, smooth emotional transitions are essential for delivering a genuine conversation experience to users. Yet, the task is challenging because emotion is too implicit and complicated to understand. Among previous studies in the domain of retrieval-based conversational model, they only consider the factors of semantic and functional dependencies of utterances. In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion. Extensive experiments on two benchmark datasets indicate that the proposed model can effectively track the flow of emotions throughout a human-machine conversation and significantly improve response selection in dialogues over the state-of-the-art baselines. We also empirically validate the emotion-control effects of our proposed model on three different emotional aspects. Finally, we apply such functionalities to a real IoT application.

Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.

To address the difficulty in clipping articles from various mobile applications (apps), we propose a novel framework called UniClip, which allows a user to snap a screen of an article to save the whole article in one place. The key task of the framework is search by screenshots , which has three challenges: (1) how to represent a screenshot; (2) how to formulate queries for effective article retrieval; and (3) how to identify the article from search results. We solve these by (1) segmenting a screenshot into structural units called blocks, (2) formulating effective search queries by considering the role of each block, and (3) aggregating the search result lists of multiple queries. To improve efficiency, we also extend our approach with learning-to-rank techniques so that we can find the desired article with only one query. Experimental results show that our approach achieves high retrieval performance ( F 1 = 0.868), which outperforms baselines based on keyword extraction and chunking methods. Learning-to-rank models improve our approach without learning by about 6%. A user study conducted to investigate the usability of UniClip reveals that ours is preferred by 21 out of 22 participants for its simplicity and effectiveness.

What people buy is an important aspect or view of lifestyles. Studying people's shopping patterns in different urban regions can not only provide valuable information for various commercial opportunities, but also enable a better understanding about urban infrastructure and urban lifestyle. In this paper, we aim to predict citywide shopping patterns. This is a challenging task due to the sparsity of the available data -- over 60% of the city regions are unknown for their shopping records. To address this problem, we incorporate another important view of human lifestyles, namely mobility patterns. With information on "where people go", we infer "what people buy". Moreover, to model the relations between regions, we exploit spatial interactions in our method. To that end, Collective Matrix Factorization (CMF) with an interaction regularization model is applied to fuse the data from multiple views or sources. Our experimental results have shown that our model outperforms the baseline methods on two standard metrics. Our prediction results on multiple shopping patterns reveal the divergent demands in different urban regions, and thus reflect key functional characteristics of a city. Furthermore, we are able to extract the connection between the two views of lifestyles, and achieve a better or novel understanding of urban lifestyles.

In this paper we address the difficulty of clipping articles from mobile apps. We propose a service called UniClip that allows a user to save the full content of an article by snapping a screenshot part of it. UniClip leverages a huge amount of indexed web data to mine the article by starting with a snapped screenshot. We propose approaches to solve three challenges: (1) how to represent a screenshot; (2) how to formulate effective queries for retrieving a full article; and (3) how to rank the best URL at the top from multiple search result lists. Experimental results indicate that our approach is effective in achieving as high an $$F_1$$ F 1 measure as 0.905, which outperforms the best of three baseline methods by 18 points.

We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.

The NTCIR INTENT task comprises two subtasks: {\em Subtopic Mining}, where systems are required to return a ranked list of {\em subtopic strings} for each given query; and {\em Document Ranking}, where systems are required to return a diversified web search result for each given query. This paper summarises the novel features of the Second INTENT task at NTCIR-10 and its main findings, and poses some questions for future diversified search evaluation.

Query suggestion is a useful tool to help users formulate better queries. Although this has been found highly useful globally, its effect on different queries may vary. In this paper, we examine the impact of query suggestion on queries of different degrees of difficulty. It turns out that query suggestion is much more useful for difficult queries than easy queries. In addition, the suggestions for difficult queries should rely less on their similarity to the original query. In this paper, we use a learning-to-rank approach to select query suggestions, based on several types of features including a query performance prediction. As query suggestion has different impacts on different queries, we propose an adaptive suggestion approach that makes suggestions only for difficult queries. We carry out experiments on real data from a search engine. Our results clearly indicate that an approach targeting difficult queries can bring higher gain than a uniform suggestion approach.

Query suggestion is a useful tool to help users express their information needs by supplying alternative queries. When evaluating the effectiveness of query suggestion algorithms, many previous studies focus on measuring whether a suggestion query is relevant or not to the input query. This assessment criterion is too simple to describe users' requirements. In this paper, we introduce two scenarios of query suggestion. The first scenario represents cases where the search result of the input query is unsatisfactory. The second scenario represents cases where the search result is satisfactory but the user may be looking for alternative solutions. Based on the two scenarios, we propose two assessment criteria. Our labeling results indicate that the new assessment criteria provide finer distinctions among query suggestions than the traditional relevance-based criterion.

We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query dimensions can be mined out by aggregating these significant lists. Experimental results show that a large number of lists do exist in the top results, and query dimensions generated by grouping these lists are useful for users to learn interesting knowledge about the queries.

Search queries are often ambiguous and/or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics.

Most existing search result diversification algorithms diversify search results in terms of a specific dimension. In this paper, we argue that search results should be diversified in a multi-dimensional way, as queries are usually ambiguous at different levels and dimensions. We first explore mining subtopics from four types of data sources, including anchor texts, query logs, search result clusters, and web sites. Then we propose a general framework that explicitly diversifies search results based on multiple dimensions of subtopics. It balances the relevance of documents with respect to the query and the novelty of documents by measuring the coverage of subtopics. Experimental results on the TREC 2009 Web track dataset indicate that combining multiple types of subtopics do help better understand user intents. By incorporating multiple types of subtopics, our models improve the diversity of search results over the sole use of one of them, and outperform two state-of-the-art models.

Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.

Web information is often presented in the form of record, e.g., a product record on a shopping website or a personal profile on a social utility website. Given a host webpage and related information needs, how to identify relevant records as well as their internal semantic structures is critical to many online information systems. Wrapper induction is one of the most effective methods for such tasks. However, most traditional wrapper techniques have issues dealing with web records since they are designed to extract information from a page, not a record. We propose a record-level wrapper system. In our system, we use a novel ``broom'' structure to represent both records and generated wrappers. With such representation, our system is able to effectively extract records and identify their internal semantics at the same time. We test our system on 16 real-life websites from four different domains. Experimental results demonstrate 99\% extraction accuracy in terms of F1-Value.

As a good complement to page content, anchor texts have been extensively used, and proven to be useful, in commercial search engines. However, anchor texts have been assumed to be independent, whether they come from the same Web site or not. Intuitively, an anchor text from unrelated Web sites should be considered as stronger evidence than that from the same site. This paper proposes two new methods to take into account the possible relationships between anchor texts. We consider two relationships in this paper: links from the same site and links from related sites. The importance assigned to the anchor texts in these two situations is discounted. Experimental results show that these two new models outperform the baseline model which assumes independence between hyperlinks.

Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries.

Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examples is to employ human experts to judge the relevance of documents. Unfortunately, it is difficult, time-consuming and costly. In this paper, we study the problem of exploiting click-through data for learning web search rankings that can be collected at much lower cost. We extract pairwise relevance preferences from a large-scale aggregated click-through dataset, compare these preferences with explicit human judgments, and use them as training examples to learn ranking functions. We find click-through data are useful and effective in learning ranking functions. A straightforward use of aggregated click-through data can outperform human judgments. We demonstrate that the strategies are only slightly affected by fraudulent clicks. We also reveal that the pairs which are very reliable, e.g., the pairs consisting of documents with large click frequency differences, are not sufficient for learning.

We present a demonstration of an interactive wrapper induction system, called Pictor, which is able to minimize labeling cost, yet extract data with high accuracy from a website. Our demonstration will introduce two proposed technologies: record-level wrappers and a wrapper-assisted labeling strategy. These approaches allow Pictor to exploit previously generated wrappers, in order to predict similar labels in a partially labeled webpage or a completely new webpage. Our experiment results show the effectiveness of the Pictor system.

Many websites have large collections of pages generated dynamically from an underlying structured source like a database. The data of a category are typically encoded into similar pages by a common script or template. In recent years, some value-added services, such as comparison shopping and vertical search in a specific domain, have motivated the research of extraction technologies with high accuracy. Almost all previous works assume that input pages of a wrapper induction system conform to a common template and they can be easily identified in terms of a common schema of URL. However, we observed that it is hard to distinguish different templates using dynamic URLs today. Moreover, since extraction accuracy heavily depends on how consistent input pages are, we argue that it is risky to determine whether pages share a common template solely based on URLs. Instead, we propose a new approach that utilizes similarity between pages to detect templates. Our approach separates pages with notable inner differences and then generates wrappers, respectively. Experimental results show that our proposed approach is feasible and effective for improving extraction accuracy.

Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study this problem and get some preliminary conclusions. We present a large-scale evaluation framework for personalized search based on query logs, and then evaluate five personalized search strategies (including two click-based and three profile-based ones) using 12-day MSN query logs. By analyzing the results, we reveal that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries (e.g., queries with small click entropy). It even harms search accuracy under some situations. Furthermore, we show that straightforward click-based personalization strategies perform consistently and considerably well, while profile-based ones are unstable in our experiments. We also reveal that both long-term and short-term contexts are very important in improving search performance for profile-based personalized search strategies.

It is widely believed that some queries submitted to search engines are by nature ambiguous (e.g., java, apple). However, few studies have investigated the questions of "how many queries are ambiguous?" and "how can we automatically identify an ambiguous query?" This paper deals with these issues. First, we construct the taxonomy of query ambiguity, and ask human annotators to manually classify queries based upon it. From manually labeled results, we find that query ambiguity is to some extent predictable. We then use a supervised learning approach to automatically classify queries as being ambiguous or not. Experimental results show that we can correctly identify 87% of labeled queries. Finally, we estimate that about 16% of queries in a real search log are ambiguous.

This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).

This paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model from concepts of information retrieval (documents, queries, relevance, etc) to those of physics (mass, distance, radius, attractive force, etc). This model actually provides a new perspective on IR problems. A family of effective term weighting functions can be derived from it, including the well-known BM25 formula. This model has some advantages over most existing ones: First, because it is directly based on basic physical laws, the derived formulas and algorithms can have their explicit physical interpretation. Second, the ranking formulas derived from this model satisfy more intuitive heuristics than most of existing ones, thus have the potential to behave empirically better and to be used safely on various settings. Finally, a new approach for structured document retrieval derived from this model is more reasonable and behaves better than existing ones.

Previous work shows that a web page can be partitioned into multiple segments or blocks, and often the importance of those blocks in a page is not equivalent. Also, it has been proven that differentiating noisy or unimportant blocks from pages can facilitate web mining, search and accessibility. However, no uniform approach and model has been presented to measure the importance of different segments in web pages. Through a user study, we found that people do have a consistent view about the importance of blocks in web pages. In this paper, we investigate how to find a model to automatically assign importance values to blocks in a web page. We define the block importance estimation as a learning problem. First, we use a vision-based page segmentation algorithm to partition a web page into semantic blocks with a hierarchical structure. Then spatial features (such as position and size) and content features (such as the number of images and links) are extracted to construct a feature vector for each block. Based on these features, learning algorithms are used to train a model to assign importance to different segments in the web page. In our experiments, the best model can achieve the performance with Micro-F1 79% and Micro-Accuracy 85.9%, which is quite close to a person's view.