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Xiang Geng

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6 papers

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AAAI Conference 2023 Conference Paper

CoP: Factual Inconsistency Detection by Controlling the Preference

Shuaijie She
Xiang Geng
Shujian Huang
Jiajun Chen

Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation model reflect its preferences for the generated summary, including the preference for factual consistency, and the preference for the language or knowledge prior as well. To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt. More specifically, the framework performs an extra inference step in which a text prompt is introduced as an additional input. In this way, another preference is described by the generation probability of this extra inference process. The difference between the above two preferences, i.e. the difference between the probabilities, could be used as measurements for detecting factual inconsistencies. Interestingly, we found that with the properly designed prompt, our framework could evaluate specific preferences and serve as measurements for fine-grained categories of inconsistency, such as entity-related inconsistency, coreference-related inconsistency, etc. Moreover, our framework could also be extended to the supervised setting to learn better prompt from the labeled data as well. Experiments show that our framework achieves new SOTA results on three factual inconsisency detection tasks.

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AAAI Conference 2023 Conference Paper

Denoising Pre-training for Machine Translation Quality Estimation with Curriculum Learning

Xiang Geng
Yu Zhang
Jiahuan Li
Shujian Huang
Hao Yang
Shimin Tao
Yimeng Chen
Ning Xie

Quality estimation (QE) aims to assess the quality of machine translations when reference translations are unavailable. QE plays a crucial role in many real-world applications of machine translation. Because labeled QE data are usually limited in scale, recent research, such as DirectQE, pre-trains QE models with pseudo QE data and obtains remarkable performance. However, there tends to be inevitable noise in the pseudo data, hindering models from learning QE accurately. Our study shows that the noise mainly comes from the differences between pseudo and real translation outputs. To handle this problem, we propose CLQE, a denoising pre-training framework for QE based on curriculum learning. More specifically, we propose to measure the degree of noise in the pseudo QE data with some metrics based on statistical or distributional features. With the guidance of these metrics, CLQE gradually pre-trains the QE model using data from cleaner to noisier. Experiments on various benchmarks reveal that CLQE outperforms DirectQE and other strong baselines. We also show that with our framework, pre-training converges faster than directly using the pseudo data. We make our CLQE code available (https://github.com/NJUNLP/njuqe).

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AAAI Conference 2021 Conference Paper

DirectQE: Direct Pretraining for Machine Translation Quality Estimation

Qu Cui
Shujian Huang
Jiahuan Li
Xiang Geng
Zaixiang Zheng
Guoping Huang
Jiajun Chen

Machine Translation Quality Estimation (QE) is a task of predicting the quality of machine translations without relying on any reference. Recently, the predictor-estimator framework trains the predictor as a feature extractor, which leverages the extra parallel corpora without QE labels, achieving promising QE performance. However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly. We propose a novel framework called DirectQE that provides a direct pretraining for QE tasks. In DirectQE, a generator is trained to produce pseudo data that is closer to the real QE data, and a detector is pretrained on these data with novel objectives that are akin to the QE task. Experiments on widely used benchmarks show that DirectQE outperforms existing methods, without using any pretraining models such as BERT. We also give extensive analyses showing how fixing the two gaps contributes to our improvements.

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IJCAI Conference 2019 Conference Paper

Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization

Wanli Shi
Bin Gu
Xiang Li
Xiang Geng
Heng Huang

Semi-supervised learning is pervasive in real-world applications, where only a few labeled data are available and large amounts of instances remain unlabeled. Since AUC is an important model evaluation metric in classification, directly optimizing AUC in semi-supervised learning scenario has drawn much attention in the machine learning community. Recently, it has been shown that one could find an unbiased solution for the semi-supervised AUC maximization problem without knowing the class prior distribution. However, this method is hardly scalable for nonlinear classification problems with kernels. To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization. In each iteration of the stochastic optimization process, our method randomly samples a positive instance, a negative instance, an unlabeled instance and their random features to compute the gradient and then update the model by using this quadruply stochastic gradient to approach the optimal solution. More importantly, we prove that QSG-S2AUC can converge to the optimal solution in O(1/t), where t is the iteration number. Extensive experimental results on a variety of benchmark datasets show that QSG-S2AUC is far more efficient than the existing state-of-the-art algorithms for semi-supervised AUC maximization, while retaining the similar generalization performance.

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IJCAI Conference 2019 Conference Paper

Scalable Semi-Supervised SVM via Triply Stochastic Gradients

Xiang Geng
Bin Gu
Xiang Li
Wanli Shi
Guansheng Zheng
Heng Huang

Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performance of the classifier. Semi-supervised support vector machine (S3VM) is one of the most appealing methods for SSL, but scaling up S3VM for kernel learning is still an open problem. Recently, a doubly stochastic gradient (DSG) algorithm has been proposed to achieve efficient and scalable training for kernel methods. However, the algorithm and theoretical analysis of DSG are developed based on the convexity assumption which makes them incompetent for non-convex problems such as S3VM. To address this problem, in this paper, we propose a triply stochastic gradient algorithm for S3VM, called TSGS3VM. Specifically, to handle two types of data instances involved in S3VM, TSGS3VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient. We use the approximated gradient to update the solution. More importantly, we establish new theoretic analysis for TSGS3VM which guarantees that TSGS3VM can converge to a stationary point. Extensive experimental results on a variety of datasets demonstrate that TSGS3VM is much more efficient and scalable than existing S3VM algorithms.

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IJCAI Conference 2018 Conference Paper

Accelerated Asynchronous Greedy Coordinate Descent Algorithm for SVMs

Bin Gu
Yingying Shan
Xiang Geng
Guansheng Zheng

Support vector machines play an important role in machine learning in the last two decades. Traditional SVM solvers (e. g. LIBSVM) are not scalable in the current big data era. Recently, a state of the art solver was proposed based on the asynchronous greedy coordinate descent (AsyGCD) algorithm. However, AsyGCD is still not scalable enough, and is limited to binary classification. To address these issues, in this paper we propose an asynchronous accelerated greedy coordinate descent algorithm (AsyAGCD) for SVMs. Compared with AsyGCD, our AsyAGCD has the following two-fold advantages: 1) our AsyAGCD is an accelerated version of AsyGCD because active set strategy is used. Specifically, our AsyAGCD can converge much faster than AsyGCD for the second half of iterations. 2) Our AsyAGCD can handle more SVM formulations (including binary classification and regression SVMs) than AsyGCD. We provide the comparison of computational complexity of AsyGCD and our AsyAGCD. Experiment results on a variety of datasets and learning applications confirm that our AsyAGCD is much faster than the existing SVM solvers (including AsyGCD).

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