Search Results for author: Mausam

Found 37 papers, 22 papers with code

A Simple, Strong and Robust Baseline for Distantly Supervised Relation Extraction

1 code implementation14 Oct 2021 Vipul Rathore, Kartikeya Badola, Mausam, Parag Singla

Apart from this scheme, however, there is not much to choose from in the DS-RE literature as most of the advances in this field are focused on improving the instance-encoding step rather than the instance-aggregation step.

Relation Extraction

MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction

1 code implementation30 Sep 2021 Tanishq Gupta, Mohd Zaki, N. M. Anoop Krishnan, Mausam

Here, we present a materials-aware language model, namely, MatSciBERT, which is trained on a large corpus of scientific literature published in the materials domain.

Language Modelling Named Entity Recognition +1

End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

1 code implementation EMNLP 2021 Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi, Mausam

We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).

Flowchart Grounded Dialog Response Generation Zero-Shot Flowchart Grounded Dialog Response Generation

Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs

no code implementations Findings (ACL) 2021 Dinesh Raghu, Atishya Jain, Mausam, Sachindra Joshi

In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.

Task-Oriented Dialogue Systems

End-to-End Neuro-Symbolic Architecture for Image-to-Image Reasoning Tasks

no code implementations6 Jun 2021 Ananye Agarwal, Pradeep Shenoy, Mausam

A key limitation is that such neural-to-symbolic models can only be trained end-to-end for tasks where the output space is symbolic.

Image Reconstruction Policy Gradient Methods

TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

1 code implementation5 May 2021 Shreshth Tuli, Rajas Bansal, Rohan Paul, Mausam

We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot.

DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction

1 code implementation17 Apr 2021 Abhyuday Bhartiya, Kartikeya Badola, Mausam

We show that these characteristics lead to a gross overestimation of the model performance.

Relation Extraction

OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction

1 code implementation EMNLP 2020 Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, Soumen Chakrabarti

This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12. 3 pts improvement in F1 over previous analyzers.

Open Information Extraction

Joint Spatio-Textual Reasoning for Answering Tourism Questions

1 code implementation28 Sep 2020 Danish Contractor, Shashank Goel, Mausam, Parag Singla

In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions.

Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces

no code implementations ICLR 2021 Yatin Nandwani, Deepanshu Jindal, Mausam, Parag Singla

Our framework uses a selection module, whose goal is to dynamically determine, for every input, the solution that is most effective for training the network parameters in any given learning iteration.

ToolNet: Using Commonsense Generalization for Predicting Tool Use for Robot Plan Synthesis

1 code implementation9 Jun 2020 Rajas Bansal, Shreshth Tuli, Rohan Paul, Mausam

When compared to a graph neural network baseline, it achieves 14-27% accuracy improvement for predicting known tools from new world scenes, and 44-67% improvement in generalization for novel objects not encountered during training.

Robotics

IMoJIE: Iterative Memory-Based Joint Open Information Extraction

1 code implementation ACL 2020 Keshav Kolluru, Samarth Aggarwal, Vipul Rathore, Mausam, Soumen Chakrabarti

While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.

Open Information Extraction

Knowledge Base Completion: Baseline strikes back (Again)

1 code implementation2 May 2020 Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti

Most existing methods train with a small number of negative samples for each positive instance in these datasets to save computational costs.

Knowledge Base Completion Knowledge Base Population +3

Why and when should you pool? Analyzing Pooling in Recurrent Architectures

1 code implementation Findings of the Association for Computational Linguistics 2020 Pratyush Maini, Keshav Kolluru, Danish Pruthi, Mausam

We find that pooling-based architectures substantially differ from their non-pooling equivalents in their learning ability and positional biases--which elucidate their performance benefits.

Text Classification

Unsupervised Learning of KB Queries in Task-Oriented Dialogs

no code implementations30 Apr 2020 Dinesh Raghu, Nikhil Gupta, Mausam

Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses.

A Simple Yet Strong Pipeline for HotpotQA

no code implementations EMNLP 2020 Dirk Groeneveld, Tushar Khot, Mausam, Ashish Sabharwal

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition.

Multi-hop Question Answering Named Entity Recognition +1

Symbolic Network: Generalized Neural Policies for Relational MDPs

no code implementations18 Feb 2020 Sankalp Garg, Aniket Bajpai, Mausam

We present SymNet, the first neural approach for solving RMDPs that are expressed in the probabilistic planning language of RDDL.

A Primal Dual Formulation For Deep Learning With Constraints

1 code implementation NeurIPS 2019 Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla

In this paper, we present a constrained optimization formulation for training a deep network with a given set of hard constraints on output labels.

Entity Typing Named Entity Recognition +3

Large Scale Question Answering using Tourism Data

no code implementations8 Sep 2019 Danish Contractor, Krunal Shah, Aditi Partap, Mausam, Parag Singla

We introduce the novel task of answering entity-seeking recommendation questions using a collection of reviews that describe candidate answer entities.

Information Retrieval Question Answering

Size Independent Neural Transfer for RDDL Planning

no code implementations8 Feb 2019 Sankalp Garg, Aniket Bajpai, Mausam

To mitigate this, recent work has studied neural transfer learning, so that a generic planner trained on other problems of the same domain can rapidly transfer to a new problem.

Transfer Learning

Transfer of Deep Reactive Policies for MDP Planning

1 code implementation NeurIPS 2018 Aniket Bajpai, Sankalp Garg, Mausam

We then learn an RL agent in the embedding space, making a near zero-shot transfer possible, i. e., without much training on the new instance, and without using the domain simulator at all.

Transfer Learning

Block-Value Symmetries in Probabilistic Graphical Models

1 code implementation2 Jul 2018 Gagan Madan, Ankit Anand, Mausam, Parag Singla

These orbits are represented compactly using permutations over variables, and variable-value (VV) pairs, but they can miss several state symmetries in a domain.

Disentangling Language and Knowledge in Task-Oriented Dialogs

1 code implementation NAACL 2019 Dinesh Raghu, Nikhil Gupta, Mausam

We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.

Language Modelling

Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models

1 code implementation27 Jul 2017 Ankit Anand, Ritesh Noothigattu, Parag Singla, Mausam

Moreover, algorithms for lifted inference in multi-valued domains also compute a multi-valued extension of count symmetries only.

Coarse-to-Fine Lifted MAP Inference in Computer Vision

1 code implementation22 Jul 2017 Haroun Habeeb, Ankit Anand, Mausam, Parag Singla

We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation.

Semantic Segmentation

Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing

1 code implementation12 Feb 2017 Karan Goel, Shreya Rajpal, Mausam

We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace - the quality of work, total cost incurred, and time to completion.

A Programming Language With a POMDP Inside

no code implementations31 Aug 2016 Christopher H. Lin, Mausam, Daniel S. Weld

We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert practitioners.

Contextual Symmetries in Probabilistic Graphical Models

no code implementations30 Jun 2016 Ankit Anand, Aditya Grover, Mausam, Parag Singla

We extend previous work on exploiting symmetries in the MCMC framework to the case of contextual symmetries.

Topological Value Iteration Algorithms

no code implementations16 Jan 2014 Peng Dai, Mausam, Daniel Sabby Weld, Judy Goldsmith

Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases.

A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains

no code implementations15 Jan 2014 Nicolas Meuleau, Emmanuel Benazera, Ronen I. Brafman, Eric A. Hansen, Mausam

We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability.

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