Query AutoComplete (QAC) is an important feature in e-commerce search engines, aimed at enhancing user experience by offering relevant query suggestions. However, these suggestions often include semantically duplicate entries derived from user logs. While the existing literature has made significant progress in query similarity learning for e-commerce applications, the specific challenge of query deduplication has received less attention. To address this issue, this paper presents a new industry-scale framework for QAC deduplication at Coupang, utilizing diverse data augmentation techniques to enhance deduplication accuracy effectively. Our results reveal that this approach substantially outperforms existing query similarity methods, providing valuable insights into the utility of various pre-trained models and data augmentation strategies. Online A/B testing further validates the significant impact of our deduplication framework on improving the e-commerce search experience, highlighting the importance of addressing semantic duplicates in QAC suggestions and offering a practical solution with proven effectiveness in a live e-commerce environment.

Multi-label classification is a learning task of predicting a set of target labels for a given example. In this paper, we propose an ensemble method for multi-label classification, which is designed to optimize a novel minimum ranking margin objective function. Moreover, a boosting-type strategy is adopted to construct an accurate multi-label ensemble from multiple weak base classifiers. Experiments on different real-world multi-label classification tasks show that better performance can be achieved compared to other well-established methods.

We present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class classification problem, and only requires very weak base classifiers; (ii) DMCBoost builds an ensemble classifier by directly optimizing the non-convex performance measures, including the empirical classification error and margin functions, without resorting to any upper bounds or approximations. As a non-convex optimization method, DMCBoost shows competitive or better results than state-of-the-art convex relaxation boosting methods, and it performs especially well on the noisy cases.

This paper proposes a multi-objective optimization framework which supports heterogeneous information sources to improve alignment in machine translation system combination techniques.In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to compact an exponential number of an potential hypotheses, and because better hypothesis alignment may benefit constructing better quality confusion networks, it is natural to add more useful information to improve alignment results.However, these information may be heterogeneous, so the widely-used Viterbi algorithm for searching the best alignment may not apply here.In the multi-objective optimization framework, each information source is viewed as an independent objective, and a new goal of improving all objectives can be searched by mature algorithms.The solutions from this framework, termed Pareto optimal solutions, are then combined to construct confusion networks.Experiments on two Chinese-to-English translation datasets show significant improvements, 0.97 and 1.06 BLEU points over a strong Indirected Hidden Markov Model-based (IHMM) system, and 4.75 and 3.53 points over the best single machine translation systems.

We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training examples; once the training classification error is reduced to a local coordinatewise minimum, DirectBoost runs a greedy coordinate ascent algorithm that continuously adds weak classifiers to maximize any targeted arbitrarily defined margins until reaching a local coordinatewise maximum of the margins in a certain sense. Experimental results on a collection of machine-learning benchmark datasets show that DirectBoost gives consistently better results than AdaBoost, LogitBoost, LPBoost with column generation and BrownBoost, and is noise tolerant when it maximizes an n'th order bottom sample margin.