146 citations · 202 across the 11 of their papers we have counts for
18 papers · 1 filter
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…
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval
Tong Niu, Kazuma Hashimoto, Yingbo Zhou +1
Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an a…
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…
Focused Attention Improves Document-Grounded Generation
Shrimai Prabhumoye, Kazuma Hashimoto, Yingbo Zhou +2
Document grounded generation is the task of using the information provided in a document to improve text generation. This work focuses on two different document grounded generation…
Neural Text Generation with Artificial Negative Examples
Keisuke Shirai, Kazuma Hashimoto, Akiko Eriguchi +2
Neural text generation models conditioning on given input (e.g. machine translation and image captioning) are usually trained by maximum likelihood estimation of target text. Howev…