publications

2024

1. Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders

   Jurek Leonhardt, Henrik Müller, Koustav Rudra , and 3 more authors

   ACM Transactions of Information Systems (TOIS), 2024

   Abs arXiv PDF

   Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency. We propose Fast-Forward indexes – vector forward indexes which exploit the semantic matching capabilities of dual-encoder models for efficient and effective re-ranking. Our framework enables re-ranking at very high retrieval depths and combines the merits of both lexical and semantic matching via score interpolation. Furthermore, in order to mitigate the limitations of dual-encoders, we tackle two main challenges: Firstly, we improve computational efficiency by either pre-computing representations, avoiding unnecessary computations altogether, or reducing the complexity of encoders. This allows us to considerably improve ranking efficiency and latency. Secondly, we optimize the memory footprint and maintenance cost of indexes; we propose two complementary techniques to reduce the index size and show that, by dynamically dropping irrelevant document tokens, the index maintenance efficiency can be improved substantially. We perform evaluation to show the effectiveness and efficiency of Fast-Forward indexes – our method has low latency and achieves competitive results without the need for hardware acceleration, such as GPUs.

2. Data Augmentation for Sample Efficient and Robust Document Ranking

   Abhijit Anand, Jurek Leonhardt, Jaspreet Singh , and 2 more authors

   ACM Transactions of Information Systems (TOIS), 2024

   Abs arXiv PDF

   Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this paper, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.

3. Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

   Lijun Lyu, Nirmal Roy, Harrie Oosterhuis , and 1 more author

   In Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part IV , 2024

   Abs arXiv PDF

   Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are much less interpretable. That is, it is very difficult to understand their inner workings and answer questions like how do they make their ranking decisions? or what document features do they find important? This is particularly disadvantageous since interpretability is highly important for real-world systems. In this work, we explore feature selection for neural learning-to-rank (LTR). In particular, we investigate six widely-used methods from the field of interpretable machine learning (ML) and introduce our own modification, to select the input features that are most important to the ranking behavior. To understand whether these methods are useful for practitioners, we further study whether they contribute to efficiency enhancement. Our experimental results reveal a large feature redundancy in several LTR benchmarks: the local selection method TabNet can achieve optimal ranking performance with less than 10 features; the global methods, particularly our G-L2X, require slightly more selected features, but exhibit higher potential in improving efficiency. We hope that our analysis of these feature selection methods will bring the fields of interpretable ML and LTR closer together.

4. Temporal Blind Spots in Large Language Models

   Jonas Wallat, Adam Jatowt, and Avishek Anand

   In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024 , 2024

   Abs arXiv PDF Code

   Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. These models, benefiting from their advanced natural language understanding capabilities, have demonstrated impressive zero-shot performance. However, the pre-training data utilized in LLMs is often confined to a specific corpus, resulting in inherent freshness and temporal scope limitations. Consequently, this raises concerns regarding the effectiveness of LLMs for tasks involving temporal intents. In this study, we aim to investigate the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding. We pay particular attention to handling factual temporal knowledge through three popular temporal QA datasets. Specifically, we observe low performance on detailed questions about the past and, surprisingly, for rather new information. In manual and automatic testing, we find multiple temporal errors and characterize the conditions under which QA performance deteriorates. Our analysis contributes to understanding LLM limitations and offers valuable insights into developing future models that can better cater to the demands of temporally-oriented tasks.

5. RankingSHAP - Listwise Feature Attribution Explanations for Ranking Models

   Maria Heuss, Maarten Rijke, and Avishek Anand

   arXiV, 2024

   arXiv PDF
6. QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims

   Venktesh V, Abhijit Anand, Avishek Anand , and 1 more author

   Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, 2024

   arXiv PDF
7. The Surprising Effectiveness of Rankers Trained on Expanded Queries

   Abhijit Anand, Venktesh V, Vinay Setty , and 1 more author

   Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, 2024

   arXiv PDF
8. Model-free reinforcement learning with noisy actions for automated experimental control in optics

   Lea Richtmann, Viktoria-S. Schmiesing, Dennis Wilken , and 5 more authors

   CoRR, 2024

   arXiv PDF
9. DINE: Dimensional Interpretability of Node Embeddings

   Simone Piaggesi, Megha Khosla, André Panisson , and 1 more author

   Transactions of Knowledge and Data Engineering (TKDE), 2024

   arXiv PDF

2023

1. An in-depth analysis of passage-level label transfer for contextual document ranking

   Koustav Rudra, Zeon Trevor Fernando, and Avishek Anand

   Information Retrieval Journmal, 2023

   Abs PDF

   Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT. Researchers proposed ranking strategies that either truncate the documents beyond the token limit or chunk the documents into units that can fit into the BERT. In the later case, the relevance labels are either directly transferred from the original query-document pair or learned through some external model. In this paper, we conduct a detailed study of the design decisions about splitting and label transfer on retrieval effectiveness and efficiency. We find that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness for large training datasets. We also find that query processing times are adversely affected by fine-grained splitting schemes. As a remedy, we propose a careful passage level labelling scheme using weak supervision that delivers improved performance (3–14% in terms of nDCG score) over most of the recently proposed models for ad-hoc retrieval while maintaining manageable computational complexity on four diverse document retrieval datasets.

2. Report on the 21st Dutch-Belgian Information Retrieval Workshop (DIR 2023)

   Avishek Anand, Maria Soledad Pera, Maria Heuss , and 2 more authors

   SIGIR Forum, 2023
3. Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks

   Thorben Funke, Megha Khosla, Mandeep Rathee , and 1 more author

   IEEE Trans. Knowl. Data Eng., 2023
4. Extractive Explanations for Interpretable Text Ranking

   Jurek Leonhardt, Koustav Rudra, and Avishek Anand

   ACM Transactions of Information Systems (TOIS), 2023

   Abs arXiv PDF

   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 parameters, these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents. In this paper we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for Select-and-Rank models utilizing reparameterizable subset sampling using the Gumbel-max trick. We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are interpretable by design. Finally, we present real-world applications that benefit from our sentence selection method.

5. Probing BERT for Ranking Abilities

   Jonas Wallat, Fabian Beringer, Abhijit Anand , and 1 more author

   In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part II , 2023

   Abs PDF Code

   Contextual models like BERT are highly effective in numerous text-ranking tasks. However, it is still unclear as to whether contextual models understand well-established notions of relevance that are central to IR. In this paper, we use probing, a recent approach used to analyze language models, to investigate the ranking abilities of BERT-based rankers. Most of the probing literature has focussed on linguistic and knowledge-aware capabilities of models or axiomatic analysis of ranking models. In this paper, we fill an important gap in the information retrieval literature by conducting a layer-wise probing analysis using four probes based on lexical matching, semantic similarity as well as linguistic properties like coreference resolution and named entity recognition. Our experiments show an interesting trend that BERT-rankers better encode ranking abilities at intermediate layers. Based on our observations, we train a ranking model by augmenting the ranking data with the probe data to show initial yet consistent performance improvements.

6. Listwise Explanations for Ranking Models Using Multiple Explainers

   Lijun Lyu, and Avishek Anand

   In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part I , 2023

   Abs PDF

   This paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a GENERALIZED PREFERENCE COVERAGE (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method MULTIPLEX uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers’ practical utility.

7. Explainability of Text Processing and Retrieval Methods

   Avishek Anand, Sourav Saha, Procheta Sen , and 1 more author

   In Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation, FIRE 2023, Panjim, India, December 15-18, 2023 , 2023
8. A Deep Reinforcement Learning Approach to Configuration Sampling Problem

   Amir Abolfazli, Jakob Spiegelberg, Gregory Palmer , and 1 more author

   In IEEE International Conference on Data Mining, ICDM 2023, Shanghai, China, December 1-4, 2023 , 2023

   Best Paper Award Abs PDF

   true

   Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the challenge of tractably finding potentially faulty configurations given exponentially large configurations. To overcome this problem, prior works focused on sampling strategies to significantly reduce the number of generated configurations, guaranteeing a high t-wise coverage. In this work, we address the configuration sampling problem by proposing a deep reinforcement learning (DRL) based sampler that efficiently finds the trade-off between exploration and exploitation, allowing for the efficient identification of a minimal subset of configurations that covers all t-wise feature interactions while minimizing redundancy. We also present the CS-Gym, an environment for the configuration sampling. We benchmark our results against heuristic-based sampling methods on eight different feature models of software product lines and show that our method outperforms all sampling methods in terms of sample size. Our findings indicate that the achieved improvement has major implications for cost reduction, as the reduction in sample size results in fewer configurations that need to be tested.

9. Explainable Information Retrieval

   Avishek Anand, Procheta Sen, Sourav Saha , and 2 more authors

   In , 2023

   arXiv PDF
10. The Effect of Masking Strategies on Knowledge Retention by Language Models

    Jonas Wallat, Tianyi Zhang, and Avishek Anand

    arxiv, 2023

    arXiv PDF
11. Query Understanding in the Age of Large Language Models

    Avishek Anand, Venktesh V, Abhijit Anand , and 1 more author

    arxiv, 2023

    arXiv PDF
12. Context Aware Query Rewriting for Text Rankers using LLM

    Abhijit Anand, Venktesh V, Vinay Setty , and 1 more author

    CoRR, 2023

    Abs PDF

    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 ranking. Queries are typically rewritten during query processing time for better query modelling for the downstream ranker. With the advent of large- language models (LLMs), there have been initial investigations into using generative approaches to generate pseudo documents to tackle this inherent vocabulary gap. In this work, we analyze the utility of LLMs for improved query rewriting for text ranking tasks. We find that there are two inherent limitations of using LLMs as query re-writers – concept drift when using only queries as prompts and large inference costs during query processing. We adopt a simple, yet surprisingly effective, approach called context aware query rewriting (CAR) to leverage the benefits of LLMs for query understanding. Firstly, we rewrite ambiguous training queries by context-aware prompting of LLMs, where we use only relevant documents as context. Unlike existing approaches, we use LLM- based query rewriting only during the training phase. Eventually, a ranker is fine-tuned on the rewritten queries instead of the original queries during training. In our extensive experiments, we find that fine-tuning a ranker using re-written queries offers a significant improvement of up to 33% on the passage ranking task and up to 28% on the document ranking task when compared to the baseline performance of using original queries.

13. In-Context Ability Transfer for Question Decomposition in Complex QA

    Venktesh V, Sourangshu Bhattacharya, and Avishek Anand

    arxiv, 2023

    arXiv
14. Data Augmentation for Sample Efficient and Robust Document Ranking

    Abhijit Anand, Jurek Leonhardt, Jaspreet Singh , and 2 more authors

    arxiv, 2023

2022

1. For What It’s Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

   Alexander Erlei, Richeek Das, Lukas Meub , and 2 more authors

   In CHI ’22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022 , 2022

   Abs PDF

   As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent’s behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm.

2. Answer Quality Aware Aggregation for Extractive QA Crowdsourcing

   Peide Zhu, Zhen Wang, Claudia Hauff , and 2 more authors

   In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , 2022

   Abs PDF

   Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing. Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality. However, crowd workers cannot reach a consensus on a considerable portion of questions. We introduce a simple yet effective answer aggregation method that takes into account the relations among the answer, question, and context passage. We evaluate answer quality from both the view of question answering model to determine how confident the QA model is about each answer and the view of the answer verification model to determine whether the answer is correct. Then we compute aggregation scores with each answer’s quality and its contextual embedding produced by pre-trained language models. The experiments on a large real crowdsourced EQA dataset show that our framework outperforms baselines by around 16% on precision and effectively conduct answer aggregation for extractive QA task.

3. Supervised Contrastive Learning Approach for Contextual Ranking

   Abhijit Anand, Jurek Leonhardt, Koustav Rudra , and 1 more author

   In ICTIR ’22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), Madrid, Spain, July 11 - 12, 2022 , 2022

   arXiv
4. BERT Rankers are Brittle: A Study using Adversarial Document Perturbations

   Yumeng Wang, Lijun Lyu, and Avishek Anand

   In ICTIR ’22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval, Madrid, Spain, July 11 - 12, 2022 , 2022

   arXiv
5. SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals

   Zijian Zhang, Vinay Setty, and Avishek Anand

   In , 2022

   arXiv
6. Efficient Neural Ranking using Forward Indexes

   Jurek Leonhardt, Koustav Rudra, Megha Khosla , and 2 more authors

   In , 2022

   arXiv
7. BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

   Mandeep Rathee, Thorben Funke, Avishek Anand , and 1 more author

   arxiv, 2022

   arXiv
8. Explainable Information Retrieval: A Survey

   Avishek Anand, Lijun Lyu, Maximilian Idahl , and 3 more authors

   arxiv, 2022

   PDF
9. Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval

   Jurek Leonhardt, Marcel Jahnke, and Avishek Anand

   CoRR, 2022

   arXiv

2021

1. Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections

   Rishiraj Saha Roy, and Avishek Anand

   2021
2. A Comparative Study for Unsupervised Network Representation Learning

   Megha Khosla, Vinay Setty, and Avishek Anand

   IEEE Trans. Knowl. Data Eng., 2021
3. FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop

   Zijian Zhang, Koustav Rudra, and Avishek Anand

   In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , 2021
4. Towards Axiomatic Explanations for Neural Ranking Models

   Michael Völske, Alexander Bondarenko, Maik Fröbe , and 4 more authors

   In ICTIR ’21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Canada, July 11, 2021 , 2021
5. Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models

   Jaspreet Singh, Megha Khosla, Zhenye Wang , and 1 more author

   In ICTIR ’21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Canada, July 11, 2021 , 2021
6. Exploiting Sentence-Level Representations for Passage Ranking

   Jurek Leonhardt, Fabian Beringer, and Avishek Anand

   In Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021 , 2021
7. L3S at the TREC 2021 Deep Learning Track

   Jurek Leonhardt, Avishek Anand, and Koustav Rudra

   In Proceedings of the Thirtieth Text REtrieval Conference, TREC 2021, online, November 15-19, 2021 , 2021
8. Explain and Predict, and then Predict Again

   Zijian Zhang, Koustav Rudra, and Avishek Anand

   In WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021 , 2021
9. Explain and Predict, and then Predict again

   Zijian Zhang, Koustav Rudra, and Avishek Anand

   CoRR, 2021
10. Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings

    Megha Khosla, and Avishek Anand

    CoRR, 2021
11. Dissonance Between Human and Machine Understanding

    Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju , and 1 more author

    CoRR, 2021
12. An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking

    Koustav Rudra, Zeon Trevor Fernando, and Avishek Anand

    CoRR, 2021
13. Towards Benchmarking the Utility of Explanations for Model Debugging

    Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju , and 1 more author

    CoRR, 2021
14. Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks

    Thorben Funke, Megha Khosla, and Avishek Anand

    CoRR, 2021
15. BERTnesia: Investigating the capture and forgetting of knowledge in BERT

    Jonas Wallat, Jaspreet Singh, and Avishek Anand

    CoRR, 2021
16. Exploiting Sentence-Level Representations for Passage Ranking

    Jurek Leonhardt, Fabian Beringer, and Avishek Anand

    CoRR, 2021
17. Towards Axiomatic Explanations for Neural Ranking Models

    Michael Völske, Alexander Bondarenko, Maik Fröbe , and 4 more authors

    CoRR, 2021
18. Learnt Sparsity for Effective and Interpretable Document Ranking

    Jurek Leonhardt, Koustav Rudra, and Avishek Anand

    CoRR, 2021
19. Learnt Sparsification for Interpretable Graph Neural Networks

    Mandeep Rathee, Zijian Zhang, Thorben Funke , and 2 more authors

    CoRR, 2021
20. Fast Forward Indexes for Efficient Document Ranking

    Jurek Leonhardt, Koustav Rudra, Megha Khosla , and 2 more authors

    CoRR, 2021

    arXiv
21. FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop

    Zijian Zhang, Koustav Rudra, and Avishek Anand

    CoRR, 2021

2020

1. Characterization and classification of semantic image-text relations

   Christian Otto, Matthias Springstein, Avishek Anand , and 1 more author

   International Journal Multimedia Information Retrieval, 2020
2. Dagstuhl seminar 19461 on conversational search: seminar goals and working group outcomes

   Avishek Anand, Lawrence Cavedon, Matthias Hagen , and 3 more authors

   SIGIR Forum, 2020
3. BERTnesia: Investigating the capture and forgetting of knowledge in BERT

   Jaspreet Singh, Jonas Wallat, and Avishek Anand

   In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020 , 2020
4. Distant Supervision in BERT-based Adhoc Document Retrieval

   Koustav Rudra, and Avishek Anand

   In CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020 , 2020
5. Model agnostic interpretability of rankers via intent modelling

   Jaspreet Singh, and Avishek Anand

   In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020 , 2020
6. Impact of Algorithmic Decision Making on Human Behavior: Evidence from Ultimatum Bargaining

   Alexander Erlei, Franck Awounang Nekdem, Lukas Meub , and 2 more authors

   In Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020, Hilversum, The Netherlands (virtual), October 25-29, 2020 , 2020
7. Question Answering over Curated and Open Web Sources

   Rishiraj Saha Roy, and Avishek Anand

   In ICTIR ’20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Norway, September 14-17, 2020 , 2020
8. Question Answering over Curated and Open Web Sources

   Rishiraj Saha Roy, and Avishek Anand

   In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020 , 2020
9. Boilerplate Removal using a Neural Sequence Labeling Model

   Jurek Leonhardt, Avishek Anand, and Megha Khosla

   In Companion of The 2020 Web Conference 2020, Taipei, Taiwan, April 20-24, 2020 , 2020
10. Question Answering over Curated and Open Web Sources

    Rishiraj Saha Roy, and Avishek Anand

    CoRR, 2020
11. Valid Explanations for Learning to Rank Models

    Jaspreet Singh, Megha Khosla, and Avishek Anand

    CoRR, 2020
12. Boilerplate Removal using a Neural Sequence Labeling Model

    Jurek Leonhardt, Avishek Anand, and Megha Khosla

    CoRR, 2020
13. Conversational Search - A Report from Dagstuhl Seminar 19461

    Avishek Anand, Lawrence Cavedon, Matthias Hagen , and 3 more authors

    CoRR, 2020
14. BERTnesia: Investigating the capture and forgetting of knowledge in BERT

    Jonas Wallat, Jaspreet Singh, and Avishek Anand

    CoRR, 2020

2019

1. A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs

   Megha Khosla, and Avishek Anand

   Algorithmica, 2019
2. Estimating PageRank deviations in crawled graphs

   Helge Holzmann, Avishek Anand, and Megha Khosla

   Appl. Netw. Sci., 2019
3. Dissonance Between Human and Machine Understanding

   Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju , and 1 more author

   Proc. ACM Hum. Comput. Interact., 2019
4. Clarifying False Memories in Voice-based Search

   Johannes Kiesel, Arefeh Bahrami, Benno Stein , and 2 more authors

   In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, CHIIR 2019, Glasgow, Scotland, UK, March 10-14, 2019 , 2019
5. Understanding, Categorizing and Predicting Semantic Image-Text Relations

   Christian Otto, Matthias Springstein, Avishek Anand , and 1 more author

   In Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR 2019, Ottawa, ON, Canada, June 10-13, 2019 , 2019

   PDF
6. Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases

   Maximilian Idahl, Megha Khosla, and Avishek Anand

   In Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I , 2019
7. Node Representation Learning for Directed Graphs

   Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl , and 1 more author

   In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I , 2019
8. A study on the Interpretability of Neural Retrieval Models using DeepSHAP

   Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand

   In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019 , 2019
9. Asynchronous Training of Word Embeddings for Large Text Corpora

   Avishek Anand, Megha Khosla, Jaspreet Singh , and 2 more authors

   In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019 , 2019
10. EXS: Explainable Search Using Local Model Agnostic Interpretability

    Jaspreet Singh, and Avishek Anand

    In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019 , 2019
11. TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

    Besnik Fetahu, Avishek Anand, and Maria Koutraki

    In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019 , 2019
12. TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

    Besnik Fetahu, Avishek Anand, and Maria Koutraki

    CoRR, 2019
13. A Comprehensive Comparison of Unsupervised Network Representation Learning Methods

    Megha Khosla, Avishek Anand, and Vinay Setty

    CoRR, 2019
14. Understanding, Categorizing and Predicting Semantic Image-Text Relations

    Christian Otto, Matthias Springstein, Avishek Anand , and 1 more author

    CoRR, 2019
15. A study on the Interpretability of Neural Retrieval Models using DeepSHAP

    Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand

    CoRR, 2019
16. Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases

    Maximilian Idahl, Megha Khosla, and Avishek Anand

    CoRR, 2019
17. Conversational Search (Dagstuhl Seminar 19461)

    Avishek Anand, Lawrence Cavedon, Hideo Joho , and 2 more authors

    Dagstuhl Reports, 2019

2018

1. Delusive PageRank in Incomplete Graphs

   Helge Holzmann, Avishek Anand, and Megha Khosla

   In Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018, Cambridge, UK, December 11-13, 2018 , 2018
2. Effects of Algorithmic Decision-Making and Interpretability on Human Behavior: Experiments using Crowdsourcing

   Avishek Anand, Kilian Bizer, Alexander Erlei , and 5 more authors

   In Proceedings of the HCOMP 2018 Works in Progress and Demonstration Papers Track of the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2018), Zurich, Switzerland, July 5-8, 2018 , 2018
3. Toward Voice Query Clarification

   Johannes Kiesel, Arefeh Bahrami, Benno Stein , and 2 more authors

   In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 , 2018
4. User Fairness in Recommender Systems

   Jurek Leonhardt, Avishek Anand, and Megha Khosla

   In Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018 , 2018
5. Posthoc Interpretability of Learning to Rank Models using Secondary Training Data

   Jaspreet Singh, and Avishek Anand

   CoRR, 2018
6. User Fairness in Recommender Systems

   Jurek Leonhardt, Avishek Anand, and Megha Khosla

   CoRR, 2018
7. EXS: Explainable Search Using Local Model Agnostic Interpretability

   Jaspreet Singh, and Avishek Anand

   CoRR, 2018
8. Interpreting search result rankings through intent modeling

   Jaspreet Singh, and Avishek Anand

   CoRR, 2018
9. Node Representation Learning for Directed Graphs

   Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl , and 1 more author

   CoRR, 2018
10. History by Diversity: Helping Historians search News Archives

    Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

    CoRR, 2018
11. Discovering Entities with Just a Little Help from You

    Jaspreet Singh, Johannes Hoffart, and Avishek Anand

    CoRR, 2018
12. Designing Search Tasks for Archive Search

    Jaspreet Singh, and Avishek Anand

    CoRR, 2018
13. Expedition: A Time-Aware Exploratory Search System Designed for Scholars

    Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

    CoRR, 2018
14. Asynchronous Training of Word Embeddings for Large Text Corpora

    Avishek Anand, Megha Khosla, Jaspreet Singh , and 2 more authors

    CoRR, 2018

2017

1. Designing Search Tasks for Archive Search

   Jaspreet Singh, and Avishek Anand

   In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, CHIIR 2017, Oslo, Norway, March 7-11, 2017 , 2017
2. Fine Grained Citation Span for References in Wikipedia

   Besnik Fetahu, Katja Markert, and Avishek Anand

   In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017 , 2017
3. BioNex: A System For Biomedical News Event Exploration

   Patrick Ernst, Arunav Mishra, Avishek Anand , and 1 more author

   In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017 , 2017
4. Exploring Web Archives Through Temporal Anchor Texts

   Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

   In Proceedings of the 2017 ACM on Web Science Conference, WebSci 2017, Troy, NY, USA, June 25 - 28, 2017 , 2017
5. Modeling Event Importance for Ranking Daily News Events

   Vinay Setty, Abhijit Anand, Arunav Mishra , and 1 more author

   In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017 , 2017
6. Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events

   Tuan Tran, Claudia Niederée, Nattiya Kanhabua , and 2 more authors

   CoRR, 2017
7. ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation

   Helge Holzmann, Vinay Goel, and Avishek Anand

   CoRR, 2017
8. Tempas: Temporal Archive Search Based on Tags

   Helge Holzmann, and Avishek Anand

   CoRR, 2017
9. The Dawn of Today’s Popular Domains: A Study of the Archived German Web over 18 Years

   Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

   CoRR, 2017
10. On the Applicability of Delicious for Temporal Search on Web Archives

    Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

    CoRR, 2017
11. Finding News Citations for Wikipedia

    Besnik Fetahu, Katja Markert, Wolfgang Nejdl , and 1 more author

    CoRR, 2017
12. Automated News Suggestions for Populating Wikipedia Entity Pages

    Besnik Fetahu, Katja Markert, and Avishek Anand

    CoRR, 2017
13. How much is Wikipedia Lagging Behind News?

    Besnik Fetahu, Abhijit Anand, and Avishek Anand

    CoRR, 2017
14. Fine Grained Citation Span for References in Wikipedia

    Besnik Fetahu, Katja Markert, and Avishek Anand

    CoRR, 2017

2016

1. History by Diversity: Helping Historians search News Archives

   Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

   In Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval, CHIIR 2016, Carrboro, North Carolina, USA, March 13-17, 2016 , 2016
2. Finding News Citations for Wikipedia

   Besnik Fetahu, Katja Markert, Wolfgang Nejdl , and 1 more author

   In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016 , 2016
3. Discovering Entities with Just a Little Help from You

   Jaspreet Singh, Johannes Hoffart, and Avishek Anand

   In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016 , 2016
4. The Dawn of Today’s Popular Domains: A Study of the Archived German Web over 18 Years

   Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

   In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, June 19 - 23, 2016 , 2016
5. ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation

   Helge Holzmann, Vinay Goel, and Avishek Anand

   In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, June 19 - 23, 2016 , 2016
6. L3S at the NTCIR-12 Temporal Information Access (Temporalia-2) Task

   Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand

   In Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, National Center of Sciences, Tokyo, Japan, June 7-10, 2016 , 2016
7. On the Applicability of Delicious for Temporal Search on Web Archives

   Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

   In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , 2016
8. Expedition: A Time-Aware Exploratory Search System Designed for Scholars

   Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

   In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , 2016
9. Temporal Information Retrieval

   Nattiya Kanhabua, and Avishek Anand

   In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , 2016
10. Exploring the past of the web: alexandria & archive-it hackathon

    Avishek Anand, and Jefferson Bailey

    In Proceedings of the 8th ACM Conference on Web Science, WebSci 2016, Hannover, Germany, May 22-25, 2016 , 2016
11. The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities

    Johannes Hoffart, Dragan Milchevski, Gerhard Weikum , and 2 more authors

    In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11-15, 2016, Companion Volume , 2016
12. Tempas: Temporal Archive Search Based on Tags

    Helge Holzmann, and Avishek Anand

    In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11-15, 2016, Companion Volume , 2016
13. A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs

    Megha Khosla, and Avishek Anand

    CoRR, 2016

2015

1. Automated News Suggestions for Populating Wikipedia Entity Pages

   Besnik Fetahu, Katja Markert, and Avishek Anand

   In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 , 2015
2. Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events

   Tuan Tran, Claudia Niederée, Nattiya Kanhabua , and 2 more authors

   In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 , 2015
3. The Sweet Spot between Inverted Indices and Metric-Space Indexing for Top-K-List Similarity Search

   Evica Milchevski, Avishek Anand, and Sebastian Michel

   In Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, March 23-27, 2015 , 2015
4. How much is Wikipedia Lagging Behind News?

   Besnik Fetahu, Abhijit Anand, and Avishek Anand

   In Proceedings of the ACM Web Science Conference, WebSci 2015, Oxford, United Kingdom, June 28 - July 1, 2015 , 2015
5. Exploring Long Running News Stories using Wikipedia

   Jaspreet Singh, Abhijit Anand, Vinay Setty , and 1 more author

   In Proceedings of the ACM Web Science Conference, WebSci 2015, Oxford, United Kingdom, June 28 - July 1, 2015 , 2015

2014

1. Phrase Query Optimization on Inverted Indexes

   Avishek Anand, Ida Mele, Srikanta J. Bedathur , and 1 more author

   In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014 , 2014

2013

1. Indexing methods for web archives

   Avishek Anand

   Saarland University , 2013
2. FERRARI: Flexible and efficient reachability range assignment for graph indexing

   Stephan Seufert, Avishek Anand, Srikanta J. Bedathur , and 1 more author

   In 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8-12, 2013 , 2013

2012

1. Index maintenance for time-travel text search

   Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

   In The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’12, Portland, OR, USA, August 12-16, 2012 , 2012
2. High-Performance Reachability Query Processing under Index Size Restrictions

   Stephan Seufert, Avishek Anand, Srikanta J. Bedathur , and 1 more author

   CoRR, 2012

2011

1. Temporal index sharding for space-time efficiency in archive search

   Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

   In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, 2011 , 2011

2010

1. Efficient temporal keyword search over versioned text

   Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

   In Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010 , 2010

2009

1. EverLast: a distributed architecture for preserving the web

   Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 2 more authors

   In Proceedings of the 2009 Joint International Conference on Digital Libraries, JCDL 2009, Austin, TX, USA, June 15-19, 2009 , 2009