Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models.
In this work, we carefully select five datasets, including two in-domain datasets and three out-of-domain datasets with different levels of domain shift, and study the generalization of a deep model in a zero-shot setting.
We further demonstrate that the dominant neural MCC architecture can be formulated as a neural ranking framework with a specific set of design choices.
We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.
We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.
To this end, we both explore two different vocabulary composition methods, as well as propose three sampling methods which help in efficient incremental training for BERT-like models.
text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document.
We investigate the privacy and utility implications of applying dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU applications.
First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing).
Ranked #1 on Text-Image Retrieval on WIT
We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.
Pre-trained models like BERT (Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering.
A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed.
In this paper, we report the results of our participation in the TREC-COVID challenge.
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area.
In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.
It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.
To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.
In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models.
Our approach effectively captures the multimodal semantics of queries and videos using state-of-the-art deep neural networks and creates a summary that is both semantically coherent and visually attractive.