184 citations · 582 across the 57 of their papers we have counts for
8 papers · 1 filter
AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
Rui Meng, Ye Liu, Semih Yavuz +6
Dense retrievers have made significant strides in text retrieval and open-domain question answering. However, most of these achievements have relied heavily on extensive human-anno…
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database
Ye Liu, Semih Yavuz, Rui Meng +3
Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski +2
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more pron…
Modeling Multi-hop Question Answering as Single Sequence Prediction
Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou +2
Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state…
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Liyan Tang, Tanya Goyal, Alexander R. Fabbri +6
The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of error…
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Man Luo, Kazuma Hashimoto, Semih Yavuz +3
While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of…
Converse: A Tree-Based Modular Task-Oriented Dialogue System
Tian Xie, Xinyi Yang, Angela S. Lin +13
Creating a system that can have meaningful conversations with humans to help accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI). It has defined the meani…