Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search engines. The main issue is that the pre-trained language model-based encoders used by DRs are typically trained and fine-tuned using clean, well-curated text data. Misspelled queries are typically not found in the data used for training these models, and thus misspelled queries observed at inference time are out-of-distribution compared to the data used for training and fine-tuning. Previous efforts to address this issue have focused on \textit{fine-tuning} strategies, but their effectiveness on misspelled queries remains lower than that of pipelines that employ separate state-of-the-art spell-checking components. To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel \textit{pre-training} strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks. ToRoDer utilizes an encoder-decoder architecture where the encoder takes misspelled text with masked tokens as input and outputs bottlenecked information to the decoder. The decoder then takes as input the bottlenecked embeddings, along with token embeddings of the original text with the misspelled tokens masked out. The pre-training task is to recover the masked tokens for both the encoder and decoder. Our extensive experimental results and detailed ablation studies show that DRs pre-trained with ToRoDer exhibit significantly higher effectiveness on misspelled queries, sensibly closing the gap with pipelines that use a separate, complex spell-checker component, while retaining their effectiveness on correctly spelled queries.
Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lacking multilingual training data. In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus. We carefully design a mining method which combines the sparse and dense models to mine the relevance of unlabeled queries and passages. And we introduce a query generator to generate more queries in target languages for unlabeled passages. Through extensive experiments on Mr. TYDI dataset and an industrial dataset from a commercial search engine, we demonstrate that our method performs better than baselines based on various pre-trained multilingual models. Our method even achieves on-par performance with the supervised method on the latter dataset.
Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.
The Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A pretrained foundation model, such as BERT, GPT-3, MAE, DALLE-E, and ChatGPT, is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. The idea of pretraining behind PFMs plays an important role in the application of large models. Different from previous methods that apply convolution and recurrent modules for feature extractions, the generative pre-training (GPT) method applies Transformer as the feature extractor and is trained on large datasets with an autoregressive paradigm. Similarly, the BERT apples transformers to train on large datasets as a contextual language model. Recently, the ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few show prompting. With the extraordinary success of PFMs, AI has made waves in a variety of fields over the past few years. Considerable methods, datasets, and evaluation metrics have been proposed in the literature, the need is raising for an updated survey. This study provides a comprehensive review of recent research advancements, current and future challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. We first review the basic components and existing pretraining in natural language processing, computer vision, and graph learning. We then discuss other advanced PFMs for other data modalities and unified PFMs considering the data quality and quantity. Besides, we discuss relevant research about the fundamentals of the PFM, including model efficiency and compression, security, and privacy. Finally, we lay out key implications, future research directions, challenges, and open problems.
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising findings in tabular pre-trained language models (TPLMs), there is an input gap between pre-training and fine-tuning phases. For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval. To this end, we propose UTP, an approach that dynamically supports three types of multi-modal inputs: table-text, table, and text. Specifically, UTP is pre-trained with two strategies: (1) We first utilize a universal mask language modeling objective on each kind of input, enforcing the model to adapt various inputs. (2) We then present Cross-Modal Contrastive Regularization (CMCR), which utilizes contrastive learning to encourage the consistency between table-text cross-modality representations via unsupervised instance-wise training signals during pre-training. By these means, the resulting model not only bridges the input gap between pre-training and fine-tuning but also advances in the alignment of table and text. Extensive results show UTP achieves superior results on uni-modal input tasks (e.g., table retrieval) and cross-modal input tasks (e.g., table question answering).
Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past few years, plenty of studies have leveraged various forms of external knowledge to augment the reasoning capabilities of deep models, tackling challenges such as effective knowledge integration, implicit knowledge mining, and problems of tractability and optimization. However, there is a dearth of a comprehensive technical review of the existing knowledge-enhanced reasoning techniques across the diverse range of application domains. This survey provides an in-depth examination of recent advancements in the field, introducing a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We systematically discuss these methods and highlight their correlations, strengths, and limitations. Finally, we elucidate the current application domains and provide insight into promising prospects for future research.
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-scale graphs. In this work, we propose to capture long-distance dependency in graphs by shallower models instead of deeper models, which leads to a much more efficient model, LazyGNN, for graph representation learning. Moreover, we demonstrate that LazyGNN is compatible with existing scalable approaches (such as sampling methods) for further accelerations through the development of mini-batch LazyGNN. Comprehensive experiments demonstrate its superior prediction performance and scalability on large-scale benchmarks. LazyGNN also achieves state-of-art performance on the OGB leaderboard.
Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmentation strategies, some fundamental questions still remain unclear: what information is essentially encoded into the learned representations by GCL? Are there some general graph augmentation rules behind different augmentations? If so, what are they and what insights can they bring? In this paper, we answer these questions by establishing the connection between GCL and graph spectrum. By an experimental investigation in spectral domain, we firstly find the General grAph augMEntation (GAME) rule for GCL, i.e., the difference of the high-frequency parts between two augmented graphs should be larger than that of low-frequency parts. This rule reveals the fundamental principle to revisit the current graph augmentations and design new effective graph augmentations. Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works. Guided by this rule, we propose a spectral graph contrastive learning module (SpCo), which is a general and GCL-friendly plug-in. We combine it with different existing GCL models, and extensive experiments well demonstrate that it can further improve the performances of a wide variety of different GCL methods.
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge. Such knowledge includes human know-how and craftsmanship that can be extremely helpful to the federated learning task. In this work, we propose a federated learning framework that allows the injection of participants' domain knowledge, where the key idea is to refine the global model with knowledge locally. The scenario we consider is motivated by a real industry-level application, and we demonstrate the effectiveness of our approach to this application.