Search Results for author: Shikhar Vashishth

Found 22 papers, 16 papers with code

A Morphology-Based Investigation of Positional Encodings

no code implementations6 Apr 2024 Poulami Ghosh, Shikhar Vashishth, Raj Dabre, Pushpak Bhattacharyya

How does the importance of positional encoding in pre-trained language models (PLMs) vary across languages with different morphological complexity?

Dependency Parsing named-entity-recognition +3

LLM Augmented LLMs: Expanding Capabilities through Composition

1 code implementation4 Jan 2024 Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar

Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains.

Arithmetic Reasoning Code Generation

Self-Influence Guided Data Reweighting for Language Model Pre-training

no code implementations2 Nov 2023 Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy, Shikhar Vashishth, Sarath Chandar, Partha Talukdar

Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training.

Language Modelling

MASR: Multi-label Aware Speech Representation

no code implementations20 Jul 2023 Anjali Raj, Shikhar Bharadwaj, Sriram Ganapathy, Min Ma, Shikhar Vashishth

In the recent years, speech representation learning is constructed primarily as a self-supervised learning (SSL) task, using the raw audio signal alone, while ignoring the side-information that is often available for a given speech recording.

Emotion Recognition Language Identification +4

Label Aware Speech Representation Learning For Language Identification

no code implementations7 Jun 2023 Shikhar Vashishth, Shikhar Bharadwaj, Sriram Ganapathy, Ankur Bapna, Min Ma, Wei Han, Vera Axelrod, Partha Talukdar

In this paper, we propose a novel framework of combining self-supervised representation learning with the language label information for the pre-training task.

Language Identification Missing Labels +3

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

no code implementations15 Dec 2021 Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.

Contrastive Learning Entity Linking

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

1 code implementation ACL 2021 Justin Lovelace, Denis Newman-Griffis, Shikhar Vashishth, Jill Fain Lehman, Carolyn Penstein Rosé

We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting.

Knowledge Graph Completion Re-Ranking

DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues

2 code implementations ICLR 2021 Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth, Alan Black, Yulia Tsvetkov

To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential.

Response Generation

MedFilter: Improving Extraction of Task-relevant Utterances from Doctor-Patient Conversations through Integration of Discourse Structure and Ontological Knowledge

1 code implementation EMNLP 2020 Sopan Khosla, Shikhar Vashishth, Jill Fain Lehman, Carolyn Rose

In this paper, we propose the novel modeling approach MedFilter, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task.

Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets

1 code implementation1 May 2020 Shikhar Vashishth, Denis Newman-Griffis, Rishabh Joshi, Ritam Dutt, Carolyn Rose

To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.

Entity Disambiguation Entity Linking +2

A Re-evaluation of Knowledge Graph Completion Methods

2 code implementations ACL 2020 Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, Yiming Yang

Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs.

Ranked #25 on Link Prediction on FB15k-237 (MR metric)

Link Prediction

Neural Graph Embedding Methods for Natural Language Processing

2 code implementations8 Nov 2019 Shikhar Vashishth

Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them.

Graph Embedding Knowledge Graphs +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

Attention Interpretability Across NLP Tasks

2 code implementations24 Sep 2019 Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, Manaal Faruqui

The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque.

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).

Clustering Feature Engineering +4

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 +1

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.

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

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