Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue. Even the state-of-the-art MLN engines can not scale to medium-size real-world knowledge bases in the open-world setting, i.e., all unobserved facts in the knowledge base need predictions. In this work, by focusing on a certain class of first-order logic rules that are sufficiently expressive, we develop a highly efficient MLN inference engine called MLN4KB that can leverage the sparsity of knowledge bases. MLN4KB enjoys quite strong theoretical properties; its space and time complexities can be exponentially smaller than existing MLN engines. Experiments on both synthetic and real-world knowledge bases demonstrate the effectiveness of the proposed method. MLN4KB is orders of magnitudes faster (more than 103 times faster on some datasets) than existing MLN engines in the open-world setting. Without any approximation tricks, MLN4KB can scale to real-world knowledge bases including WN-18 and YAGO3-10 and achieve decent prediction accuracy without bells and whistles.

Entity relations and concepts are the most critical information in knowledge-based systems. In traditional closed-domain knowledge bases (KBs), the entity relations and concepts are tightly bound together in the human-designed schema. The relations of entities (relations in the KB) are correlated with its concepts (entity types in the KB). However, the relations and concepts described by the closed-domain KBs are limited. When we extend the investigation to the open domain, we find that the relations of entities (from Open Information Extraction data sets) and the concepts of entities (from large-scale concept graphs) reside in the different data sources, and there is no connection between them. In this paper, we proposed a matching network-based concept graph completion model, which leverages open-domain relations to represent the query entity to predict the entity's concept, where relations are extracted from the open-domain corpus (e.g., Wikipedia). By comparing with other neural baselines, which leverage the whole sentences, we show that our model gets superior performance. Furthermore, we use the open-domain relations as the explanation basis and build an explanation model to answer the question “why an entity belongs to a concept”. The model gives clear and informative explanations with high relevance to human understanding.

Click-through rate (CTR) prediction is crucial for online sponsored search advertising. Several successful CTR models have been adopted in the industry, including the regularized logistic regression (LR). Nonetheless, the learning process suffers from two limitations: 1) Feature crosses for high-order information may generate trillions of features, which are sparse for online learning examples; 2) Rapid changing of data distribution brings challenges to the accurate learning since the model has to perform a fast adaptation on the new data. Moreover, existing adaptive optimizers are ineffective in handling the sparsity issue for high-dimensional features.

Due to their attractiveness, video advertisements are adored by advertisers. Baidu, as one of the leading search advertisement platforms in China, is putting more and more effort into video advertisements for its advertisement customers. Search-based video advertisement display is, in essence, a cross-modal retrieval problem, which is normally tackled through joint embedding methods. Nevertheless, due to the lack of interactions between text features and image features, joint embedding methods cannot achieve as high accuracy as its counterpart based on attention. Inspired by the great success achieved by BERT in NLP tasks, many cross-modal BERT models emerge and achieve excellent performance in cross-modal retrieval. Last year, Baidu also launched a cross-modal BERT, CAN, in video advertisement platform, and achieved considerably better performance than the previous joint-embedding model. In this paper, we present our recent work for video advertisement retrieval, Multi-modal Dictionary BERT (MDBERT) model. Compared with CAN and other cross-modal BERT models, MDBERT integrates a joint dictionary, which is shared among video features and word features. It maps the relevant word features and video features into the same codeword and thus fosters effective cross-modal attention. To support end-to-end training, we propose to soften the codeword assignment. Meanwhile, to enhance the inference efficiency, we adopt the product quantization to achieve fine-level feature space partition at a low cost. After launching MDBERT in Baidu video advertising platform, the conversion ratio (CVR) increases by 3.34%, bringing a considerable revenue boost for advertisers in Baidu.

Causal-effect discovery plays an essential role in many disciplines of science and real-world applications. In this paper, we introduce a new causal discovery method to solve the classic problem of inferring the causal direction under a bivariate setting. In particular, our proposed method first leverages a flow model to estimate the joint probability density of the variables. Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation. By leveraging the flow-based conditional density estimation metric, our causal discovery approach alleviates the restrictive assumptions made by the conventional methods, such as assuming the linearity relationship between the two variables. Therefore, it could potentially be able to better capture the complex causal relationship among data in various problem domains that comes in arbitrary forms. We conduct extensive evaluations to compare our method with decent causal discovery approaches. Empirical results show that our method could promisingly outperform the baseline methods with noticeable margins on both synthetic and real-world datasets.

By reading reviews and product attributes, e-commerce question-answering task aims to automatically generate natural-sounding answers for product-related questions. Existing methods, however, typically assume that each review and each product attribute are semantically independent, ignoring the relation among all these multi-type texts. In this paper, we propose a review-attribute heterogeneous graph neural network (abbreviated as RAHGNN) to model the logical relation of all multi-type text. RAHGNN consists of four components: a review-attribute heterogeneous graph constructor, a question-aware input encoder, a heterogeneous graph relation analyzer, and a context-based answer decoder. Specifically, after constructing the heterogeneous graph with reviews and product attributes, we derive the initial representation of each review node and attribute node based on question attention network and key-value memory network respectively. RAHGNN analyzes the relation according to the subgraph structure and subgraph semantic meaning using node-level attention and semantic-level attention. Finally, the answer is generated by the recurrent neural network with the relation representation as context input. Extensive experimental results on a large-scale real-world e-commerce dataset not only show the superior performance of RAHGNN over state-of-the-art baselines, but also demonstrate its potentially good interpretability for multi-type text relation in product-aware answer generation.

The task of knowledge graph embedding (KGE) tries to find appropriate representations for entities and relations and appropriate mathematical computations between the representations to approximate the symbolic and logical relationships between entities. One major challenge for KGE is that the relations in real-world knowledge bases exhibit complex behaviors: they can be injective (1-1) or non-injective (1-N, N-1, or N-N), symmetry or skew-symmetry; one relation may be the inversion of another relation; one relation may be the composition of other two relations (where the composition can be either Abelian or non-Abelian). To our knowledge, there has not been any theoretical guarantee that these complex behaviors can be modeled by existing KGE methods.

It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.

With the progress of communication technology and the popularity of the smart phone, videos grow to be the largest medium. Since videos can grab a customer's attention quickly and leave a big impression, video ads can gain more trust than traditional ads. Thus advertisers start to pour more resources into making creative video ads to built the connections with potential customers. Baidu, as the leading search engine company in China, receives billions of search queries per day. In this paper, we introduce a technique used in Baidu video advertising for feeding relevant video ads according to the user's query. Note that, retrieving relevant videos using the text query is a cross-modal problem. Due to the modal gap, the text-to-video search is more challenging than well exploited text-to-text search and image-to-image search. To tackle this challenge, we propose a Combo-Attention Network (CAN) and launch it in Baidu video advertising. In the proposed CAN model, we represent a video as a set of bounding boxes features and represent a sentence as a set of words features, and formulate the sentence-to-video search as a set-to-set matching problem. The proposed CAN is built upon the proposed combo-attention module, which exploits cross-modal attentions besides self attentions to effectively capture the relevance between words and bounding boxes. To testify the effectiveness of the proposed CAN offline, we built a Daily700K dataset collected from HaoKan APP. The systematic experiments on Daily700K as well as a public dataset, VATEX, demonstrate the effectiveness of our CAN. After launching the proposed CAN in Baidu's dynamic video advertising (DVA), we achieve a $5.47%$ increase in Conversion Rate (CVR) and a $11.69%$ increase in advertisement impression rate.

Open Information Extraction (OIE) is a task of generating the structured representations of information from natural language sentences. Recently years, many works have trained an End-to-End OIE extractor based on Sequence-to-Sequence (Seq2Seq) model and applied Reinforce Algorithm to update the model. However, the model performance often suffers from a large training variance and limited exploration. This paper introduces a reinforcement learning framework that enables an Advantage Actor-Critic (AAC) algorithm to update the Seq2Seq model with samples from a novel Confidence Exploration (CE). The AAC algorithm reduces the training variance with a fine-grained evaluation of each individual word. The confidence exploration provides effective training samples by exploring the word at key positions. Empirical evaluations demonstrate the leading performance of our Advantage Actor-Critic algorithm and Confidence Exploration over other comparison methods.

To extract knowledge from general web text, it requires to build a domain-independent extractor that scales to the entire web corpus. This task is known as Open Information Extraction (OIE). This paper proposes to apply Monte-Carlo Tree Search (MCTS) to accomplish OIE. To achieve this goal, we define a Markov Decision Process for OIE and build a simulator to learn the reward signals, which provides a complete reinforcement learning framework for MCTS. Using this framework, MCTS explores candidate words (and symbols) under the guidance of a pre-trained Sequence-to-Sequence (Seq2Seq) predictor and generates abundant exploration samples during training. We apply the exploration samples to update the reward simulator and the predictor, based on which we implement another MCTS to search the optimal predictions during inference. Empirical evaluation demonstrates that the MCTS inference substantially improves the accuracy of prediction (more than 10%) and achieves a leading performance over other state-of-the-art comparison models.

Recent years have witnessed the continuing growth of people’s dependence on touchscreen devices. As a result, input speed with the onscreen keyboard has become crucial to communication efficiency and user experience. In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME). Taken input efficiency as the optimization target, we proposed a neural end-to-end candidates generation solution to handle automatic correction, reordering, insertion, deletion as well as completion. Evaluation metrics are also discussed base on real use scenarios. For a more thorough comparison, we also provide a statistical strategy for mapping touch coordinate sequences to text input candidates. The proposed model and baselines are evaluated on a real-world dataset. The experiment (conducted on the PaddlePaddle deep learning platform1) shows that the proposed model outperforms the baselines.

Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest appearing in a free-text sentence. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation leaving a large proportion of unrecognizable long-tail relations due to insufficient labeled instances for training. In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. Specifically, we adopt the large-margin ProtoNet with fine-grained features, expecting they can generalize well on long-tail relations. Extensive experiments were conducted by FewRel, a large-scale supervised few-shot RC dataset, to evaluate our framework: LM-ProtoNet (FGF). The results demonstrate that it can achieve substantial improvements over many baseline approaches.

Baidu runs the largest commercial web search engine in China, serving hundreds of millions of online users every day in response to a great variety of queries. In order to build a high-efficiency sponsored search engine, we used to adopt a three-layer funnel-shaped structure to screen and sort hundreds of ads from billions of ad candidates subject to the requirement of low response latency and the restraints of computing resources. Given a user query, the top matching layer is responsible for providing semantically relevant ad candidates to the next layer, while the ranking layer at the bottom concerns more about business indicators (e.g., CPM, ROI, etc.) of those ads. The clear separation between the matching and ranking objectives results in a lower commercial return. The Mobius project has been established to address this serious issue. It is our first attempt to train the matching layer to consider CPM as an additional optimization objective besides the query-ad relevance, via directly predicting CTR (click-through rate) from billions of query-ad pairs. Specifically, this paper will elaborate on how we adopt active learning to overcome the insufficiency of click history at the matching layer when training our neural click networks offline, and how we use the SOTA ANN search technique for retrieving ads more efficiently (Here "ANN'' stands for approximate nearest neighbor search). We contribute the solutions to Mobius-V1 as the first version of our next generation query-ad matching system.

The e-commerce websites are ready to build the community question answering (CQA) service, as it can facilitate questioners (potential buyers) to obtain satisfying answers from experienced customers and furthermore stimulate consumption. Given that more than 50% product-related questions only anticipate a binary response (i.e., “Yes” or “No”), the research on product-related question answering (PQA), which aims to automatically provide instant and correct replies to questioners, emerges rapidly. The mainstream approaches on PQA generally employ customer reviews as the evidence to help predict answers to the questions which are product-specific and concerned more about subjective personal experiences. However, the supportive features either extracted by heuristic rules or acquired from unsupervised manners are not able to perform well on PQA. In this paper, we contribute an end-to-end neural architecture directly fed by the raw text of product-related questions and customer reviews to predict the answers. Concretely, it teaches machines to generate and to synthesize multiple question-aware review representations in a reading comprehension fashion to make the final decision. We also extract a real-world dataset crawled from 9 categories in Amazon.com for PQA to assess the performance of our neural reading architecture (NRA) and other mainstream approaches such as COR-L [12], MOQA [12], and AAP [21]. Experimental results show that our NRA sets up a new state-of-the-art performance on this dataset, significantly outperforming existing algorithms.

Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.

Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.

Paraphrase identification (PI) aims at determining whether two natural language sentences roughly have identical meaning. PI has been conventionally formalized as a binary classification task and widely used in many talks such as text summarization, plagiarism detection, etc. The emergence of deep neural networks (DNNs) renovates and dominates the learning paradigm of PI, as DNNs do not rely on lexical nor syntactic knowledge of a language, unlike traditional methods. State-of-the-art DNNs-based approaches to PI mainly adopt multi-layer convolutional neural networks (CNNs) to model paraphrastic sentences, which could discover alignments of phrases with the same length (unigram-to-unigram, bigram-to-bigram, trigram-to-trigram, etc.) at each layer. However, paraphrasing phenomena globally exist at all levels of granularity between a pair of paraphrastic sentences, i.e., word-to-word, word-to-phrase, phrase-to-phrase, and even sentence-to-sentence.

Mobile devices (e.g., smartphones) play a crucial role in our daily lives nowadays. People rely heavily on mobile devices for searching online, sending emails, chatting with friends, etc. As a result, input efficiency becomes increasingly important for real-time communication on mobile devices. Due to the small size of the screen on mobile devices, however, it is oftentimes frustrating for users to correct or update the input sequences on an even smaller input area on the screen. This often causes poor user experience. In this paper, we focus on improving the input efficiency on mobile devices to offer better user experience. In order to achieve efficient input, there are multiple challenges: 1) how to employ a single, unified representation of the keyboard layouts for different input languages; 2) how to build a framework to correct a mistouch immediately and predict the coming input texts (words or phrases) effectively; 3) how to deploy and evaluate the model on mobile devices with limited computational power. To address these challenges, we introduce \em FastInput to improve the user input efficiency on mobile devices. Three key techniques are developed in FastInput -- layout modeling, instant mistouch correction and user input text prediction. We also design solutions for efficient deployment and evaluation of FastInput on mobile devices. The proposed FastInput achieves higher efficiency compared to the traditional input system over millions of user input sequences in different languages.

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.