Weizhu Chen

ReasoNet: Learning to Stop Reading in Machine Comprehension

Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.

Document Transformation for Multi-label Feature Selection in Text Categorization

Feature selection on multi-label documents for automatic text categorization is an under-explored research area. This paper presents a systematic document transformation framework, whereby the multi-label documents are transformed into single-label documents before applying standard feature selection algorithms, to solve the multi-label feature selection problem. Under this framework, we undertake a comparative study on four intuitive document transformation approaches and propose a novel approach called entropy-based label assignment (ELA), which assigns the labels weights to a multi-label document based on label entropy. Three standard feature selection algorithms are utilized for evaluating the document transformation approaches in order to verify its impact on multi-class text categorization problems. Using a SVM classifier and two multi-label evaluation benchmark text collections, we show that the choice of document transformation approaches can significantly influence the performance of multi-class categorization and that our proposed document transformation approach ELA can achieve better performance than all other approaches.

A novel click model and its applications to online advertising

Recent advances in click model have positioned it as an attractive method for representing user preferences in web search and online advertising. Yet, most of the existing works focus on training the click model for individual queries, and cannot accurately model the tail queries due to the lack of training data. Simultaneously, most of the existing works consider the query, url and position, neglecting some other important attributes in click log data, such as the local time. Obviously, the click through rate is different between daytime and midnight. In this paper, we propose a novel click model based on Bayesian network, which is capable of modeling the tail queries because it builds the click model on attribute values, with those values being shared across queries. We called our work General Click Model (GCM) as we found that most of the existing works can be special cases of GCM by assigning different parameters. Experimental results on a large-scale commercial advertisement dataset show that GCM can significantly and consistently lead to better results as compared to the state-of-the-art works.

P-packSVM: Parallel Primal grAdient desCent Kernel SVM

It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the Support Vector Machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/¿ dependency in complexity to obtain a solution of optimization error ¿. The algorithm can be highly parallelized with a special packing strategy, and experiences sub-linear speed-up with hundreds of processors. We demonstrate that P-packSVM achieves accuracy sufficiently close to that of SVM-light, and overwhelms the state-of-the-art parallel SVM trainer PSVM in both accuracy and efficiency. As an illustration, our algorithm trains CCAT dataset with 800k samples in 13 minutes and 95% accuracy, while PSVM needs 5 hours but only has 92% accuracy. We at last demonstrate the capability of P-packSVM on 8 million training samples.

Characterizing search intent diversity into click models

Modeling a users click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examination hypothesis which distinguishes document relevance from position bias. In this paper, we revisit the examination hypothesis and observe that user clicks cannot be completely explained by relevance and position bias. Specifically, users with different search intents may submit the same query to the search engine but expect different search results. Thus, there might be a bias between user search intent and the query formulated by the user, which can lead to the diversity in user clicks. This bias has not been considered in previous works such as UBM, DBN and CCM. In this paper, we propose a new intent hypothesis as a complement to the examination hypothesis. This hypothesis is used to characterize the bias between the user search intent and the query in each search session. This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance. Experimental results demonstrate that after adopting the intent hypothesis, click models can better interpret user clicks and achieve a significant NDCG improvement.

Beyond ten blue links: enabling user click modeling in federated web search

Click models have been positioned as an effective approach to interpret user click behavior in search engines. Existing click models mostly focus on traditional Web search that considers only ten homogeneous Web HTML documents that appear on the first search-result page. However, in modern commercial search engines, more and more Web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search. We observed that user click behavior in federated search is highly different from that in traditional Web search, making it difficult to interpret using existing click models. In response, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we take into considerations two new biases in FCM. The first comes from the observation that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. The other illustrates that user click behavior on vertical results may lead to more clues of search relevance due to their presentation style in federated search. With these biases and an effective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood.

Incorporating post-click behaviors into a click model

Much work has attempted to model a users click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some break-throughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a users behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs.

Learning click models via probit bayesian inference

Recent advances in click models have positioned them as an effective approach to the improvement of interpreting click data, and some typical works include UBM, DBN, CCM, etc. After formulating the knowledge of user search behavior into a set of model assumptions, each click model developed an inference method to estimate its parameters. The inference method plays a critical role in terms of accuracy in interpreting clicks, and we observe that different inference methods for a click model can lead to significant accuracy differences. In this paper, we propose a novel Bayesian inference approach for click models. This approach regards click model under a unified framework, which has the following characteristics and advantages: 1. This approach can be widely applied to existing click models, and we demonstrate how to infer DBN, CCM and UBM through it. This novel inference method is based on the Bayesian framework which is more flexible in characterizing the uncertainty in clicks and brings higher generalization abilities. As a result, it not only excels in the inference methods originally developed in click models, but also provides a valid comparison among different models; 2. In contrast to the previous click models, which are exclusively designed for the position-bias, this approach is capable of capturing more sophisticated information such as BM25 and PageRank score into click models. This makes these models interpret click-through data more accurately. Experimental results illustrate that the click models integrated with more information can achieve significantly better performance on click perplexity and search ranking; 3. Because of the incremental nature of the Bayesian learning, this approach is scalable to process large scale and constantly growing log data.

Web query translation via web log mining

This paper describes a method to automatically acquire query translation pairs by mining web click-through data. The extraction requires no crawling or Chinese words segmentation, and can capture popular translations. Experimental results on a real click-through data show that only 17.4% of the extracted queries are in the dictionary, and our method can achieve 62.2% (in top-1) to 80.0% (in top-5) precision in translating web queries. Moreover, the extracted translations are semantically relevant to the source query, which is particularly useful for Cross-Lingual Information Retrieval (CLIR).

A noise-aware click model for web search

Recent advances in click model have established it as an attractive approach to infer document relevance. Most of these advances consider the user click/skip behavior as binary events but neglect the context in which a click happens. We show that real click behavior in industrial search engines is often noisy and not always a good indication of relevance. For a considerable percentage of clicks, users select what turn out to be irrelevant documents and these clicks should not be directly used as evidence for relevance inference. Thus in this paper, we put forward an observation that the relevance indication degree of a click is not a constant, but can be differentiated by user preferences and the context in which the user makes her click decision. In particular, to interpret the click behavior discriminatingly, we propose a Noise-aware Click Model (NCM) by characterizing the noise degree of a click, which indicates the quality of the click for inferring relevance. Specifically, the lower the click noise is, the more important the click is in its role for relevance inference. To verify the necessity of explicitly accounting for the uninformative noise in a user click, we conducted experiments on a billion-scale dataset. Extensive experimental results demonstrate that as compared with two state-of-the-art click models in Web Search, NCM can better interpret user click behavior and achieve significant improvements in terms of both perplexity and NDCG.