b'Yashar Mehdad'

research

∙ 05/29/2023

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Several post-training quantization methods have been applied to large la...

0 Zechun Liu, et al. ∙

research

∙ 05/04/2023

VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation

We propose a new two-stage pre-training framework for video-to-text gene...

0 Xilun Chen, et al. ∙

research

∙ 02/15/2023

How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Various techniques have been developed in recent years to improve dense ...

0 Sheng-Chieh Lin, et al. ∙

research

∙ 12/24/2022

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension

Abstractive dialogue summarization has long been viewed as an important ...

0 Borui Wang, et al. ∙

research

∙ 12/19/2022

Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences

Lack of factual correctness is an issue that still plagues state-of-the-...

0 Asish Ghoshal, et al. ∙

research

∙ 11/18/2022

CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval

Multi-vector retrieval methods combine the merits of sparse (e.g. BM25) ...

0 Minghan Li, et al. ∙

research

∙ 10/25/2022

Bridging the Training-Inference Gap for Dense Phrase Retrieval

Building dense retrievers requires a series of standard procedures, incl...

0 Gyuwan Kim, et al. ∙

research

∙ 09/28/2022

Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task

Text segmentation aims to divide text into contiguous, semantically cohe...

0 Hakan Inan, et al. ∙

research

∙ 09/21/2022

Adapting Pretrained Text-to-Text Models for Long Text Sequences

We present an empirical study of adapting an existing pretrained text-to...

5 Wenhan Xiong, et al. ∙

research

∙ 05/25/2022

BiT: Robustly Binarized Multi-distilled Transformer

Modern pre-trained transformers have rapidly advanced the state-of-the-a...

0 Zechun Liu, et al. ∙

research

∙ 12/16/2021

CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning

Factual inconsistencies in generated summaries severely limit the practi...

8 Xiangru Tang, et al. ∙

research

∙ 12/14/2021

Simple Local Attentions Remain Competitive for Long-Context Tasks

Many NLP tasks require processing long contexts beyond the length limit ...

0 Wenhan Xiong, et al. ∙

research

∙ 10/13/2021

Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

Despite their recent popularity and well known advantages, dense retriev...

0 Xilun Chen, et al. ∙

research

∙ 09/19/2021

Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries

Current pre-trained models applied to summarization are prone to factual...

9 Xiangru Tang, et al. ∙

research

∙ 07/28/2021

Domain-matched Pre-training Tasks for Dense Retrieval

Pre-training on larger datasets with ever increasing model size is now a...

0 Barlas Oguz, et al. ∙

research

∙ 06/03/2021

Syntax-augmented Multilingual BERT for Cross-lingual Transfer

In recent years, we have seen a colossal effort in pre-training multilin...

0 Wasi Uddin Ahmad, et al. ∙

research

∙ 06/01/2021

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

While online conversations can cover a vast amount of information in man...

10 Alexander R. Fabbri, et al. ∙

research

∙ 05/14/2021

EASE: Extractive-Abstractive Summarization with Explanations

Current abstractive summarization systems outperform their extractive co...

4 Haoran Li, et al. ∙

research

∙ 01/01/2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition...

15 Sewon Min, et al. ∙

research

∙ 12/31/2020

FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation

Natural language (NL) explanations of model predictions are gaining popu...

0 Kushal Lakhotia, et al. ∙

research

∙ 12/30/2020

Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA

While research on explaining predictions of open-domain QA systems (ODQA...

0 Ana Valeria Gonzalez, et al. ∙

research

∙ 12/29/2020

Towards Understanding the Optimal Behaviors of Deep Active Learning Algorithms

Active learning (AL) algorithms may achieve better performance with fewe...

15 Yilun Zhou, et al. ∙

research

∙ 12/29/2020

Unified Open-Domain Question Answering with Structured and Unstructured Knowledge

We study open-domain question answering (ODQA) with structured, unstruct...

0 Barlas Oguz, et al. ∙

research

∙ 10/24/2020

Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation

Models pretrained with self-supervised objectives on large text corpora ...

11 Alexander R. Fabbri, et al. ∙

research

∙ 10/21/2020

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

State-of-the-art Machine Reading Comprehension (MRC) models for Open-dom...

0 Srinivasan Iyer, et al. ∙

research

∙ 10/07/2020

Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing

Task-oriented semantic parsing is a critical component of virtual assist...

0 Xilun Chen, et al. ∙

research

∙ 10/06/2020

Efficient One-Pass End-to-End Entity Linking for Questions

We present ELQ, a fast end-to-end entity linking model for questions, wh...

0 Belinda Z. Li, et al. ∙

research

∙ 09/28/2020

Conversational Semantic Parsing

The structured representation for semantic parsing in task-oriented assi...

0 Armen Aghajanyan, et al. ∙

research

∙ 09/27/2020

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

We propose a simple and efficient multi-hop dense retrieval approach for...

0 Wenhan Xiong, et al. ∙

research

∙ 08/21/2020

MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark

Scaling semantic parsing models for task-oriented dialog systems to new ...

0 Haoran Li, et al. ∙

research

∙ 07/14/2017

DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

Tagging news articles or blog posts with relevant tags from a collection...

0 Sheng Chen, et al. ∙

research

∙ 05/16/2017

Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem

The content ranking problem in a social news website, is typically a fun...

0 Jeya Balaji Balasubramanian, et al. ∙

research

∙ 02/24/2017

Rank-to-engage: New Listwise Approaches to Maximize Engagement

For many internet businesses, presenting a given list of items in an ord...

0 Swayambhoo Jain, et al. ∙

research

∙ 02/16/2017

RIPML: A Restricted Isometry Property based Approach to Multilabel Learning

The multilabel learning problem with large number of labels, features, a...

0 Akshay Soni, et al. ∙

research

∙ 12/01/2016

Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings

Content on the Internet is heterogeneous and arises from various domains...

0 Vivek Kulkarni, et al. ∙

Yashar Mehdad

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