Search Results for author: Partha Talukdar

Found 54 papers, 34 papers with code

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer

1 code implementation27 Oct 2021 Ameet Deshpande, Partha Talukdar, Karthik Narasimhan

While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer.

Cross-Lingual Transfer

Few-shot Controllable Style Transfer for Low-Resource Settings: A Study in Indian Languages

no code implementations14 Oct 2021 Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, Partha Talukdar

To facilitate further research in formality transfer for Indic languages, we crowdsource annotations for 4000 sentence pairs in four languages, and use this dataset to design our automatic evaluation suite.

Style Transfer Text Simplification

Multilingual Fact Linking

1 code implementation AKBC 2021 Keshav Kolluru, Martin Rezk, Pat Verga, William W. Cohen, Partha Talukdar

This makes it challenging to link KG facts to sentences in languages other than the limited set of languages.


MergeDistill: Merging Pre-trained Language Models using Distillation

no code implementations5 Jun 2021 Simran Khanuja, Melvin Johnson, Partha Talukdar

Pre-trained multilingual language models (LMs) have achieved state-of-the-art results in cross-lingual transfer, but they often lead to an inequitable representation of languages due to limited capacity, skewed pre-training data, and sub-optimal vocabularies.

Cross-Lingual Transfer Knowledge Distillation

Question Answering Over Temporal Knowledge Graphs

1 code implementation ACL 2021 Apoorv Saxena, Soumen Chakrabarti, Partha Talukdar

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG.

Knowledge Graphs Question Answering

Reordering Examples Helps during Priming-based Few-Shot Learning

1 code implementation Findings (ACL) 2021 Sawan Kumar, Partha Talukdar

Finally, we analyze the learned prompts to reveal novel insights, including the idea that two training examples in the right order alone can provide competitive performance for sentiment classification and natural language inference.

Few-Shot Learning Natural Language Inference +1

MuRIL: Multilingual Representations for Indian Languages

no code implementations19 Mar 2021 Simran Khanuja, Diksha Bansal, Sarvesh Mehtani, Savya Khosla, Atreyee Dey, Balaji Gopalan, Dilip Kumar Margam, Pooja Aggarwal, Rajiv Teja Nagipogu, Shachi Dave, Shruti Gupta, Subhash Chandra Bose Gali, Vish Subramanian, Partha Talukdar

This can be explained by the fact that multilingual language models (LMs) are often trained on 100+ languages together, leading to a small representation of IN languages in their vocabulary and training data.

Spatial Reasoning from Natural Language Instructions for Robot Manipulation

no code implementations26 Dec 2020 Sagar Gubbi Venkatesh, Anirban Biswas, Raviteja Upadrashta, Vikram Srinivasan, Partha Talukdar, Bharadwaj Amrutur

Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language.

NILE : Natural Language Inference with Faithful Natural Language Explanations

1 code implementation ACL 2020 Sawan Kumar, Partha Talukdar

In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions?

Decision Making Natural Language Inference

P-SIF: Document Embeddings Using Partition Averaging

1 code implementation18 May 2020 Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar

One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation.

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

1 code implementation NeurIPS 2019 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

Improving Document Classification with Multi-Sense Embeddings

1 code implementation18 Nov 2019 Vivek Gupta, Ankit Saw, Pegah Nokhiz, Harshit Gupta, Partha Talukdar

Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks.

Classification Document Classification +3

InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions

1 code implementation1 Nov 2019 Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Nilesh Agrawal, Partha Talukdar

In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE.

Knowledge Graph Embeddings Knowledge Graphs +1

CaRe: Open Knowledge Graph Embeddings

no code implementations IJCNLP 2019 Swapnil Gupta, Sreyash Kenkre, Partha Talukdar

Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs).

Knowledge Graph Embeddings Knowledge Graphs +2

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

1 code implementation ACL 2019 Sawan Kumar, Sharmistha Jat, Karan Saxena, Partha Talukdar

To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.

Generalized Zero-Shot Learning Knowledge Graph Embedding +1

Relating Simple Sentence Representations in Deep Neural Networks and the Brain

1 code implementation ACL 2019 Sharmistha Jat, Hao Tang, Partha Talukdar, Tom Mitchell

To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence.

Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation

1 code implementation NAACL 2019 Ashutosh Kumar, Satwik Bhattamishra, Bh, Manik ari, Partha Talukdar

Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.

Data Augmentation Intent Classification

CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information

1 code implementation1 Feb 2019 Shikhar Vashishth, Prince Jain, Partha Talukdar

Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs).

Feature Engineering Noun Phrase Canonicalization +2

Dating Documents using Graph Convolution Networks

1 code implementation ACL 2018 Shikhar Vashishth, Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar

While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web.

Document Dating Event Detection

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

1 code implementation24 Jan 2019 Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar

Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph.

AD3: Attentive Deep Document Dater

1 code implementation EMNLP 2018 Swayambhu Nath Ray, Shib Sankar Dasgupta, Partha Talukdar

Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc.

Document Dating Event Extraction

RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information

1 code implementation EMNLP 2018 Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar

In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.

Relationship Extraction (Distant Supervised)

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

1 code implementation14 Nov 2018 Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar

Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.

Band Gap Formation Energy +1

HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

1 code implementation EMNLP 2018 Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar

Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods.

Information Retrieval Knowledge Graph Embedding +2

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

1 code implementation7 Sep 2018 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

Lovasz Convolutional Networks

1 code implementation29 May 2018 Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar

We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.

Multi-class Classification

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

no code implementations18 Feb 2018 Madhav Nimishakavi, Bamdev Mishra, Manish Gupta, Partha Talukdar

Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time.

CANDiS: Coupled & Attention-Driven Neural Distant Supervision

no code implementations26 Oct 2017 Tushar Nagarajan, Sharmistha, Partha Talukdar

The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data.

Relation Extraction

KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs

no code implementations EMNLP 2017 Prakhar Ojha, Partha Talukdar

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently.

Knowledge Graphs

Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods

1 code implementation EMNLP 2017 Aditya Sharma, Zarana Parekh, Partha Talukdar

RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process.

Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation

1 code implementation ACL 2018 Madhav Nimishakavi, Partha Talukdar

Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text.

KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs

no code implementations21 Oct 2016 Prakhar Ojha, Partha Talukdar

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently.

Knowledge Graphs

Want Answers? A Reddit Inspired Study on How to Pose Questions

no code implementations6 Dec 2015 Danish, Yogesh Dahiya, Partha Talukdar

We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question.

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