Search Results for author:
Found 29 papers, 7 papers with code
Searching Personal Collections
This article describes the history of information retrieval on personal document collections.
Creator Context for Tweet Recommendation
In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet.
End-to-End Query Term Weighting
These inferred weights and terms can be used directly by a retrieval system to perform a query search.
Ranked #1 on
Passage Retrieval
on MS MARCO
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label.
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution.
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations
With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation.
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.
DSI++: Updating Transformer Memory with New Documents
In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents.
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval 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.
Data-Efficient Information Extraction from Form-Like Documents
Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare.
Rank4Class: A Ranking Formulation for Multiclass Classification
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.
Improving Neural Ranking via Lossless Knowledge Distillation
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.
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank
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.
Dynamic Language Models for Continuously Evolving Content
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.
Rethinking Search: Making Domain Experts out of Dilettantes
When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead.
Natural Language Understanding with Privacy-Preserving BERT
We investigate the privacy and utility implications of applying dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU applications.
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing).
Ranked #1 on
Image Retrieval
on WIT
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees
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.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling
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.
Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach
A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed.
Representation Learning for Information Extraction from Form-like Documents
We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.
Active Learning for Skewed Data Sets
Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems.
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
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.
Learning-to-Rank with BERT in TF-Ranking
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
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.
