241 citations · 802 across the 37 of their papers we have counts for
43 papers
DRAMA: Domain Retrieval using Adaptive Module Allocation
Pranav Kasela, Marco Braga, Ophir Frieder +3
Neural models are increasingly used in Web-scale Information Retrieval (IR). However, relying on these models introduces substantial computational and energy requirements, leading…
GRIT: Graph-based Recall Improvement for Task-oriented E-commerce Queries
Hrishikesh Kulkarni, Surya Kallumadi, Sean MacAvaney +2
Many e-commerce search pipelines have four stages, namely: retrieval, filtering, ranking, and personalized-reranking. The retrieval stage must be efficient and yield high recall be…
LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Sparse retrieval methods like BM25 are based on lexical overlap, focusing on the surface form of the terms that appear in the query and the document. The use of inverted indices in…
Genetic Approach to Mitigate Hallucination in Generative IR
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Generative language models hallucinate. That is, at times, they generate factually flawed responses. These inaccuracies are particularly insidious because the responses are fluent…
Lexically-Accelerated Dense Retrieval
Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian +1
Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular…
QontSum: On Contrasting Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. The broader field of Generative Informa…
Curriculum-Guided Abstractive Summarization
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent Transformer-based summarization models have provided a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal w…
Curriculum-guided Abstractive Summarization for Mental Health Online Posts
Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy +1
Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely respons…
MentSum: A Resource for Exploring Summarization of Mental Health Online Posts
Sajad Sotudeh, Nazli Goharian, Zachary Young
Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health condit…
TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation
Sajad Sotudeh, Nazli Goharian
Many scientific papers such as those in arXiv and PubMed data collections have abstracts with varying lengths of 50-1000 words and average length of approximately 200 words, where…
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data. While most exis…
Simplified Data Wrangling with ir_datasets
Sean MacAvaney, Andrew Yates, Sergey Feldman +3
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, the…
ToxCCIn: Toxic Content Classification with Interpretability
Tong Xiang, Sean MacAvaney, Eugene Yang +1
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans. Explanations are particular…
On Generating Extended Summaries of Long Documents
Sajad Sotudeh, Arman Cohan, Nazli Goharian
Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it…
ABNIRML: Analyzing the Behavior of Neural IR Models
Sean MacAvaney, Sergey Feldman, Nazli Goharian +2
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search. However, it is not yet well-understood why these methods ar…
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
Sean MacAvaney, Arman Cohan, Nazli Goharian
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinici…
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
Michael Kranzlein, Shabnam Behzad, Nazli Goharian
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique c…
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Sajad Sotudeh, Tong Xiang, Hao-Ren Yao +4
Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes thre…
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them…
Training Curricula for Open Domain Answer Re-Ranking
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking str…
Expansion via Prediction of Importance with Contextualization
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that:…
Interaction Matching for Long-Tail Multi-Label Classification
Sean MacAvaney, Franck Dernoncourt, Walter Chang +2
We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be u…
SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
Sean MacAvaney, Arman Cohan, Nazli Goharian
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, resear…
Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
Sajad Sotudeh, Nazli Goharian, Ross W. Filice
Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively ide…