2022-12-19 更新
Experiments on Generalizability of BERTopic on Multi-Domain Short Text
Authors:Muriël de Groot, Mohammad Aliannejadi, Marcel R. Haas
Topic modeling is widely used for analytically evaluating large collections of textual data. One of the most popular topic techniques is Latent Dirichlet Allocation (LDA), which is flexible and adaptive, but not optimal for e.g. short texts from various domains. We explore how the state-of-the-art BERTopic algorithm performs on short multi-domain text and find that it generalizes better than LDA in terms of topic coherence and diversity. We further analyze the performance of the HDBSCAN clustering algorithm utilized by BERTopic and find that it classifies a majority of the documents as outliers. This crucial, yet overseen problem excludes too many documents from further analysis. When we replace HDBSCAN with k-Means, we achieve similar performance, but without outliers.
PDF Accepted poster presentation at WiNLP 2022, as a part of EMNLP 2022, 2 pages
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CD-TTA: Compound Domain Test-time Adaptation for Semantic Segmentation
Authors:Junha Song, Kwanyong Park, Inkyu Shin, Sanghyun Woo, In So Kweon
Test-time adaptation (TTA) has attracted significant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage. Prior works on TTA assume that the target dataset comes from the same distribution and thus constitutes a single homogeneous domain. In practice, however, the target domain can contain multiple homogeneous domains which are sufficiently distinctive from each other and those multiple domains might occur cyclically. Our preliminary investigation shows that domain-specific TTA outperforms vanilla TTA treating compound domain (CD) as a single one. However, domain labels are not available for CD, which makes domain-specific TTA not practicable. To this end, we propose an online clustering algorithm for finding pseudo-domain labels to obtain similar benefits as domain-specific configuration and accumulating knowledge of cyclic domains effectively. Moreover, we observe that there is a significant discrepancy in terms of prediction quality among samples, especially in the CD context. This further motivates us to boost its performance with gradient denoising by considering the image-wise similarity with the source distribution. Overall, the key contribution of our work lies in proposing a highly significant new task compound domain test-time adaptation (CD-TTA) on semantic segmentation as well as providing a strong baseline to facilitate future works to benchmark.
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Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation
Authors:Hao Yan, Yuhong Guo
Unsupervised domain adaptation reduces the reliance on data annotation in deep learning by adapting knowledge from a source to a target domain. For privacy and efficiency concerns, source-free domain adaptation extends unsupervised domain adaptation by adapting a pre-trained source model to an unlabeled target domain without accessing the source data. However, most existing source-free domain adaptation methods to date focus on the transductive setting, where the target training set is also the testing set. In this paper, we address source-free domain adaptation in the more realistic inductive setting, where the target training and testing sets are mutually exclusive. We propose a new semi-supervised fine-tuning method named Dual Moving Average Pseudo-Labeling (DMAPL) for source-free inductive domain adaptation. We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model. Then we propose a soft-label moving-average updating strategy for the unlabeled subset based on a moving-average prototypical classifier, which gradually adapts the source model towards the target domain. Experiments show that our proposed method achieves state-of-the-art performance and outperforms previous methods by large margins.
PDF BMVC 2022
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Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
Authors:Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data. Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks. However, knowledge bases (KBs) utilized in these studies are usually large-scale and static, in contrast to small, domain-specific, and modifiable knowledge bases that are prominent in real-world task-oriented dialogue (TOD) systems. In this paper, we showcase the advantages of injecting domain-specific knowledge prior to fine-tuning on TOD tasks. To this end, we utilize light-weight adapters that can be easily integrated with PLMs and serve as a repository for facts learned from different KBs. To measure the efficacy of proposed knowledge injection methods, we introduce Knowledge Probing using Response Selection (KPRS) — a probe designed specifically for TOD models. Experiments on KPRS and the response generation task show improvements of knowledge injection with adapters over strong baselines.
PDF Published at EMNLP 2022 (main conference)
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Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
Authors:Sungsu Hur, Inkyu Shin, Kwanyong Park, Sanghyun Woo, In So Kweon
Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approach this problem by first training the target adapted known classifier and then relying on the single threshold to distinguish unknown target samples. However, this simple threshold-based approach prevents the model from considering the underlying complexities existing between the known and unknown samples in the high-dimensional feature space. In this paper, we propose a new approach in which we use two sets of feature points, namely dual Classifiers for Prototypes and Reciprocals (CPR). Our key idea is to associate each prototype with corresponding known class features while pushing the reciprocals apart from these prototypes to locate them in the potential unknown feature space. The target samples are then classified as unknown if they fall near any reciprocals at test time. To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection. We then additionally apply the entropy loss regularization to them. For further adaptation, we also apply standard consistency regularization that matches the predictions of two different views of the input to make more compact target feature space. We evaluate our proposal, CPR, on three standard benchmarks and achieve comparable or new state-of-the-art results. We also provide extensive ablation experiments to verify our main design choices in our framework.
PDF Accepted at WACV 2023
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