Search Results for author: Rajarshi Das

Found 27 papers, 11 papers with code

Machine Reading Comprehension using Case-based Reasoning

no code implementations24 May 2023 Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Rajarshi Das, Manzil Zaheer, Jay-Yoon Lee, Hannaneh Hajishirzi, Andrew McCallum

Given a target question, CBR-MRC retrieves a set of similar questions from a memory of observed cases and predicts an answer by selecting the span in the target context that is most similar to the contextualized representations of answers in the retrieved cases.

Machine Reading Comprehension

When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

1 code implementation20 Dec 2022 Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.

Knowledge Probing Memorization +1

Calibration of Machine Reading Systems at Scale

no code implementations Findings (ACL) 2022 Shehzaad Dhuliawala, Leonard Adolphs, Rajarshi Das, Mrinmaya Sachan

We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings.

Claim Verification Open-Domain Question Answering +2

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

1 code implementation22 Feb 2022 Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Robin Jia, Manzil Zaheer, Hannaneh Hajishirzi, Andrew McCallum

Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed.

Knowledge Base Question Answering

An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

no code implementations Findings of the Association for Computational Linguistics 2020 Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O{'}Gorman, Rajarshi Das, Andrew McCallum

Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric.

Semantic Parsing

A Simple Approach to Case-Based Reasoning in Knowledge Bases

1 code implementation AKBC 2020 Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

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 classical artificial intelligence (AI).

Knowledge Graphs Meta-Learning

Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

no code implementations WS 2019 Rajarshi Das, Ameya Godbole, Manzil Zaheer, Shehzaad Dhuliawala, Andrew McCallum

This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).

Do Multi-hop Readers Dream of Reasoning Chains?

1 code implementation WS 2019 Haoyu Wang, Mo Yu, Xiaoxiao Guo, Rajarshi Das, Wenhan Xiong, Tian Gao

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 derive the answer.

Question Answering

Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering

no code implementations WS 2019 Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Hamed Zamani, Mo Yu, Tian Gao, Xiaoxiao Guo, Manzil Zaheer, Andrew McCallum

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 retrieval process very challenging.

Information Retrieval Multi-hop Question Answering +2

A Survey on Semantic Parsing

no code implementations AKBC 2019 Aishwarya Kamath, Rajarshi Das

A significant amount of information in today's world is stored in structured and semi-structured knowledge bases.

Program Synthesis Semantic Parsing

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

no code implementations ICLR 2019 Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum

We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans.

Knowledge Graphs Machine Reading Comprehension +2

Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading

no code implementations27 Apr 2018 Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes

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 various types of texts, documents or even from large collections of them.

Open-Domain Question Answering Reading Comprehension

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

7 code implementations ICLR 2018 Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.


Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

2 code implementations EACL 2017 Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum

Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.

Logical Reasoning

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