117 citations · 367 across the 21 of their papers we have counts for
23 papers
Overview of the TREC 2022 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +4
This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels…
Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels a…
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
Daniel Campos, Alessandro Magnani, ChengXiang Zhai
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymm…
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
Daniel Campos, ChengXiang Zhai, Alessandro Magnani
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document rank…
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
Daniel Campos, ChengXiang Zhai
Sequence-to-sequence language models can be used to produce abstractive summaries which are coherent, relevant, and concise. Still, model sizes can make deployment in latency-sensi…
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
Daniel Campos, ChengXiang Zhai
Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to…
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes
Daniel Campos, Alexandre Marques, Mark Kurtz +1
In this paper, we introduce the range of oBERTa language models, an easy-to-use set of language models which allows Natural Language Processing (NLP) practitioners to obtain betwee…
Compressing Cross-Lingual Multi-Task Models at Qualtrics
Daniel Campos, Daniel Perry, Samir Joshi +4
Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experienc…
Sparse*BERT: Sparse Models Generalize To New tasks and Domains
Daniel Campos, Alexandre Marques, Tuan Nguyen +2
Large Language Models have become the core architecture upon which most modern natural language processing (NLP) systems build. These models can consistently deliver impressive acc…
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
Eldar Kurtic, Daniel Campos, Tuan Nguyen +5
Transformer-based language models have become a key building block for natural language processing. While these models are extremely accurate, they can be too large and computation…
IMG2SMI: Translating Molecular Structure Images to Simplified Molecular-input Line-entry System
Daniel Campos, Heng Ji
Like many scientific fields, new chemistry literature has grown at a staggering pace, with thousands of papers released every month. A large portion of chemistry literature focuses…
Curriculum learning for language modeling
Daniel Campos
Language Models like ELMo and BERT have provided robust representations of natural language, which serve as the language understanding component for a diverse range of downstream t…
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big ques…
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +3
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that…
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard
Jimmy Lin, Daniel Campos, Nick Craswell +2
Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as…
Overview of the TREC 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +1
This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime. We again have a document retrieval task and a p…
ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search
Nick Craswell, Daniel Campos, Bhaskar Mitra +2
Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval. However, click logs have not been…
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang, Nan Duan, Yeyun Gong +21
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evalu…
On the Reliability of Test Collections for Evaluating Systems of Different Types
Emine Yilmaz, Nick Craswell, Bhaskar Mitra +1
As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their qu…
Overview of the TREC 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets,…
Open Domain Web Keyphrase Extraction Beyond Language Modeling
Lee Xiong, Chuan Hu, Chenyan Xiong +2
This paper studies keyphrase extraction in real-world scenarios where documents are from diverse domains and have variant content quality. We curate and release OpenKP, a large sca…
Experiments in Inferring Social Networks of Diffusion
Daniel Campos, Zoe Konrad
Information diffusion is a fundamental process that takes place over networks. While it is rarely realistic to observe the individual transmissions of the information diffusion pro…
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Payal Bajaj, Daniel Campos, Nick Craswell +12
We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query…