Manzil Zaheer

Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

2019 · arXiv (Cornell University)

This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only …

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

2017 · arXiv (Cornell University)

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new …

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

2018

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In …

Big Bird: Transformers for Longer Sequences

2020 · arXiv (Cornell University)

Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence …

Case-based Reasoning for Natural Language Queries over Knowledge Bases

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.