398 citations · 763 across the 22 of their papers we have counts for
25 papers
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
Dhruv Agarwal, Rajarshi Das, Sopan Khosla +1
We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within…
Machine Reading Comprehension using Case-based Reasoning
Dung Thai, Dhruv Agarwal, Mudit Chaudhary +6
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our meth…
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Alex Mallen, Akari Asai, Victor Zhong +3
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying sol…
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Dung Thai, Srinivas Ravishankar, Ibrahim Abdelaziz +7
Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs…
Calibration of Machine Reading Systems at Scale
Shehzaad Dhuliawala, Leonard Adolphs, Rajarshi Das +1
In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction. This confidence measure is usuall…
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
Rajarshi Das, Ameya Godbole, Ankita Naik +5
Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed. However, we hypothesize in a la…
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard, Tim O'Gorman, Rajarshi Das +6
At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text.…
Case-based Reasoning for Natural Language Queries over Knowledge Bases
Rajarshi Das, Manzil Zaheer, Dung Thai +6
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reas…
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna +7
Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods ar…
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
Rajarshi Das, Ameya Godbole, Nicholas Monath +2
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing ow…
A Simple Approach to Case-Based Reasoning in Knowledge Bases
Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala +2
We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classi…
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko, Xiang Lorraine Li, Rajarshi Das +3
Given questions regarding some prototypical situation such as Name something that people usually do before they leave the house for work? a human can easily answer them via acquire…
Do Multi-hop Readers Dream of Reasoning Chains?
Haoyu Wang, Mo Yu, Xiaoxiao Guo +3
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to deriv…
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das +8
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieva…
Optimal Transport-based Alignment of Learned Character Representations for String Similarity
Derek Tam, Nicholas Monath, Ari Kobren +3
String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE --a learned model for computing the similarity of two strings.…
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer +1
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 th…
A Survey on Semantic Parsing
Aishwarya Kamath, Rajarshi Das
A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must…
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan +2
We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure…
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
Michael Boratko, Harshit Padigela, Divyendra Mikkilineni +10
The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set…
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das +1
This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from va…
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer +5
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 pop…
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
Rajarshi Das, Manzil Zaheer, Siva Reddy +1
Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, th…
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Rajarshi Das, Arvind Neelakantan, David Belanger +1
Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex…
Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation
Craig Boutilier, Rajarshi Das, Jeffrey O. Kephart +2
Decentralized resource allocation is a key problem for large-scale autonomic (or self-managing) computing systems. Motivated by a data center scenario, we explore efficient techniq…