60 citations · 547 across the 40 of their papers we have counts for
44 papers
Context Aware Query Rewriting for Text Rankers using LLM
Abhijit Anand, Venktesh V, Vinay Setty +1
Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranki…
Query Understanding in the Age of Large Language Models
Avishek Anand, Venktesh V, Abhijit Anand +1
Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language mod…
The Effect of Masking Strategies on Knowledge Retention by Language Models
Jonas Wallat, Tianyi Zhang, Avishek Anand
Language models retain a significant amount of world knowledge from their pre-training stage. This allows knowledgeable models to be applied to knowledge-intensive tasks prevalent…
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval
Jurek Leonhardt, Marcel Jahnke, Avishek Anand
Dual-encoder-based neural retrieval models achieve appreciable performance and complement traditional lexical retrievers well due to their semantic matching capabilities, which mak…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Mandeep Rathee, Thorben Funke, Avishek Anand +1
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with gra…
BERT Rankers are Brittle: a Study using Adversarial Document Perturbations
Yumeng Wang, Lijun Lyu, Avishek Anand
Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under…
Efficient Neural Ranking using Forward Indexes
Jurek Leonhardt, Koustav Rudra, Megha Khosla +2
Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized…
Learnt Sparsification for Interpretable Graph Neural Networks
Mandeep Rathee, Zijian Zhang, Thorben Funke +2
Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as ind…
Extractive Explanations for Interpretable Text Ranking
Jurek Leonhardt, Koustav Rudra, Avishek Anand
Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of…
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
Jonas Wallat, Jaspreet Singh, Avishek Anand
Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this article, we probe BERT sp…
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Thorben Funke, Megha Khosla, Mandeep Rathee +1
With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. E…
Dissonance Between Human and Machine Understanding
Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju +1
Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, ther…
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
Jonas Wallat, Jaspreet Singh, Avishek Anand
Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT spec…
Conversational Search -- A Report from Dagstuhl Seminar 19461
Avishek Anand, Lawrence Cavedon, Matthias Hagen +3
Dagstuhl Seminar 19461 "Conversational Search" was held on 10-15 November 2019. 44~researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer…
Valid Explanations for Learning to Rank Models
Jaspreet Singh, Zhenye Wang, Megha Khosla +1
Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread applicatio…
Boilerplate Removal using a Neural Sequence Labeling Model
Jurek Leonhardt, Avishek Anand, Megha Khosla
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, t…
Question Answering over Curated and Open Web Sources
Rishiraj Saha Roy, Avishek Anand
The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language process…
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
Maximilian Idahl, Megha Khosla, Avishek Anand
In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representa…
A study on the Interpretability of Neural Retrieval Models using DeepSHAP
Zeon Trevor Fernando, Jaspreet Singh, Avishek Anand
A recent trend in IR has been the usage of neural networks to learn retrieval models for text based adhoc search. While various approaches and architectures have yielded significan…
Understanding, Categorizing and Predicting Semantic Image-Text Relations
Christian Otto, Matthias Springstein, Avishek Anand +1
Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The a…
A Comparative Study for Unsupervised Network Representation Learning
Megha Khosla, Vinay Setty, Avishek Anand
There has been appreciable progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization ob…
TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables
Besnik Fetahu, Avishek Anand, Maria Koutraki
Wikipedia tables represent an important resource, where information is organized w.r.t table schemas consisting of columns. In turn each column, may contain instance values that po…
Asynchronous Training of Word Embeddings for Large Text Corpora
Avishek Anand, Megha Khosla, Jaspreet Singh +2
Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over hug…