Hong Xu


2022

pdf bib
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning
Yutao Mou | Keqing He | Yanan Wu | Zhiyuan Zeng | Hong Xu | Huixing Jiang | Wei Wu | Weiran Xu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is how to transfer prior IND knowledge to OOD clustering. Different from existing work based on shared intent representation, we propose a novel disentangled knowledge transfer method via a unified multi-head contrastive learning framework. We aim to bridge the gap between IND pre-training and OOD clustering. Experiments and analysis on two benchmark datasets show the effectiveness of our method.

2021

pdf bib
Adversarial Self-Supervised Learning for Out-of-Domain Detection
Zhiyuan Zeng | Keqing He | Yuanmeng Yan | Hong Xu | Weiran Xu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system. Previous unsupervised OOD detection methods only extract discriminative features of different in-domain intents while supervised counterparts can directly distinguish OOD and in-domain intents but require extensive labeled OOD data. To combine the benefits of both types, we propose a self-supervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data. Besides, we introduce an adversarial augmentation neural module to improve the efficiency and robustness of contrastive learning. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.

pdf bib
Data Cleaning Tools for Token Classification Tasks
Karthik Muthuraman | Frederick Reiss | Hong Xu | Bryan Cutler | Zachary Eichenberger
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Human-in-the-loop systems for cleaning NLP training data rely on automated sieves to isolate potentially-incorrect labels for manual review. We have developed a novel technique for flagging potentially-incorrect labels with high sensitivity in named entity recognition corpora. We incorporated our sieve into an end-to-end system for cleaning NLP corpora, implemented as a modular collection of Jupyter notebooks built on extensions to the Pandas DataFrame library. We used this system to identify incorrect labels in the CoNLL-2003 corpus for English-language named entity recognition (NER), one of the most influential corpora for NER model research. Unlike previous work that only looked at a subset of the corpus’s validation fold, our automated sieve enabled us to examine the entire corpus in depth. Across the entire CoNLL-2003 corpus, we identified over 1300 incorrect labels (out of 35089 in the corpus). We have published our corrections, along with the code we used in our experiments. We are developing a repeatable version of the process we used on the CoNLL-2003 corpus as an open-source library.

pdf bib
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System
Yanan Wu | Zhiyuan Zeng | Keqing He | Hong Xu | Yuanmeng Yan | Huixing Jiang | Weiran Xu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, a reliable dialogue system should know what it does not know. In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system. NSD aims to discover unknown or out-of-domain slot types to strengthen the capability of a dialogue system based on in-domain training data. Besides, we construct two public NSD datasets, propose several strong NSD baselines, and establish a benchmark for future work. Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide new guidance for future directions.

pdf bib
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning
Zhiyuan Zeng | Keqing He | Yuanmeng Yan | Zijun Liu | Yanan Wu | Hong Xu | Huixing Jiang | Weiran Xu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative semantic features. Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between categories. In this paper, we propose a supervised contrastive learning objective to minimize intra-class variance by pulling together in-domain intents belonging to the same class and maximize inter-class variance by pushing apart samples from different classes. Besides, we employ an adversarial augmentation mechanism to obtain pseudo diverse views of a sample in the latent space. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for OOD detection.

2020

pdf bib
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space
Hong Xu | Keqing He | Yuanmeng Yan | Sihong Liu | Zijun Liu | Weiran Xu
Proceedings of the 28th International Conference on Computational Linguistics

Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system. Different from most existing methods that rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this paper, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional distribution on feature spaces of DNNs via Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of whether a test sample belongs to OOD. Experiments on four benchmark datasets show that our method can consistently outperform the baselines.

pdf bib
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots
Yuanmeng Yan | Keqing He | Hong Xu | Sihong Liu | Fanyu Meng | Min Hu | Weiran Xu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit. In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence. Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.

pdf bib
Identifying Incorrect Labels in the CoNLL-2003 Corpus
Frederick Reiss | Hong Xu | Bryan Cutler | Karthik Muthuraman | Zachary Eichenberger
Proceedings of the 24th Conference on Computational Natural Language Learning

The CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research. A large number of publications, including many landmark works, have used this corpus as a source of ground truth for NER tasks. In this paper, we examine this corpus and identify over 1300 incorrect labels (out of 35089 in the corpus). In particular, the number of incorrect labels in the test fold is comparable to the number of errors that state-of-the-art models make when running inference over this corpus. We describe the process by which we identified these incorrect labels, using novel variants of techniques from semi-supervised learning. We also summarize the types of errors that we found, and we revisit several recent results in NER in light of the corrected data. Finally, we show experimentally that our corrections to the corpus have a positive impact on three state-of-the-art models.