203 citations · 849 across the 38 of their papers we have counts for
42 papers
LLM Augmented LLMs: Expanding Capabilities through Composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia +6
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. However, due to their…
Self-Influence Guided Data Reweighting for Language Model Pre-training
Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy +3
Language Models (LMs) pre-trained with self-supervision on large text corpora have become the default starting point for developing models for various NLP tasks. Once the pre-train…
Multimodal Modeling For Spoken Language Identification
Shikhar Bharadwaj, Min Ma, Shikhar Vashishth +10
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language ide…
Label Aware Speech Representation Learning For Language Identification
Shikhar Vashishth, Shikhar Bharadwaj, Sriram Ganapathy +5
Speech representation learning approaches for non-semantic tasks such as language recognition have either explored supervised embedding extraction methods using a classifier model…
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder, Jonathan H. Clark, Alexander Gutkin +24
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is part…
Salient Span Masking for Temporal Understanding
Jeremy R. Cole, Aditi Chaudhary, Bhuwan Dhingra +1
Salient Span Masking (SSM) has shown itself to be an effective strategy to improve closed-book question answering performance. SSM extends general masked language model pretraining…
Potential energy surface prediction of Alumina polymorphs using graph neural network
Soumya Sanyal, Arun Kumar Sagotra, Narendra Kumar +9
The process of design and discovery of new materials can be significantly expedited and simplified if we can learn effectively from available data. Deep learning (DL) approaches ha…
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta +3
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remain…
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning
Kartikeya Badola, Shachi Dave, Partha Talukdar
We introduce and study the problem of Continual Multilingual Learning (CML) where a previously trained multilingual model is periodically updated using new data arriving in stages.…
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages
Vaidehi Patil, Partha Talukdar, Sunita Sarawagi
Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However…
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
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 comm…
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings
Kalpesh Krishna, Deepak Nathani, Xavier Garcia +2
Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled…
Multilingual Fact Linking
Keshav Kolluru, Martin Rezk, Pat Verga +2
Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact label…
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil +3
Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resou…
MuRIL: Multilingual Representations for Indian Languages
Simran Khanuja, Diksha Bansal, Sarvesh Mehtani +11
India is a multilingual society with 1369 rationalized languages and dialects being spoken across the country (INDIA, 2011). Of these, the 22 scheduled languages have a staggering…
Spatial Reasoning from Natural Language Instructions for Robot Manipulation
Sagar Gubbi Venkatesh, Anirban Biswas, Raviteja Upadrashta +3
Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language. We propose a pipelined architec…
NILE : Natural Language Inference with Faithful Natural Language Explanations
Sawan Kumar, Partha Talukdar
The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanatio…
P-SIF: Document Embeddings Using Partition Averaging
Vivek Gupta, Ankit Saw, Pegah Nokhiz +3
Simple weighted averaging of word vectors often yields effective representations for sentences which outperform sophisticated seq2seq neural models in many tasks. While it is desir…
Syntax-guided Controlled Generation of Paraphrases
Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli +1
Given a sentence (e.g., "I like mangoes") and a constraint (e.g., sentiment flip), the goal of controlled text generation is to produce a sentence that adapts the input sentence to…
Improving Document Classification with Multi-Sense Embeddings
Vivek Gupta, Ankit Saw, Pegah Nokhiz +2
Efficient representation of text documents is an important building block in many NLP tasks. Research on long text categorization has shown that simple weighted averaging of word v…
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classificat…
A Re-evaluation of Knowledge Graph Completion Methods
Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal +2
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got publishe…
Composition-based Multi-Relational Graph Convolutional Networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin +1
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undire…
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin +2
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generat…