Modern learning-to-rank methods often rely on listwise objectives that directly model and optimize relative document order over entire permutations. While these objectives improve ranking quality, they frequently produce models with highly inflated relevance scores whose magnitudes exceed what is necessary for meaningful document separation, leading to poor probabilistic calibration.

Arkadeep Acharya, Rudra Murthy, Vishwajeet Kumar, Jaydeep Sen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Sshubam Verma, Mohammed Safi Ur Rahman Khan, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

Natural Language Query interfaces allow the end-users to access the desired information without the need to know any specialized query language, data storage, or schema details.Even with the recent advances in NLP research space, the state-of-the-art QA systems fall short of understanding implicit intents of real-world Business Intelligence (BI) queries in enterprise systems as Natural Language Understanding remains an AI-hard problem.We posit that deploying ontology reasoning over domain semantics can help in achieving better natural language understanding for QA systems.In this paper, we specifically focus on building a Schema Aware Semantic Reasoning Framework that translates natural language interpretation as a sequence of solvable tasks by an ontology reasoner.We apply our framework on top of an ontology-based, state-of-the-art natural language question-answering system ATHENA, and experiment with 4 benchmarks focused on BI queries.Our experimental numbers empirically show that the Schema Aware Semantic Reasoning indeed helps in achieving significantly better results for handling BI queries with an average accuracy improvement of 30%

Recent advances in natural language understanding and processing resulted in renewed interest in natural language based interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems only allowed simple selection queries over a single table, some recent work supports complex BI queries, with many joins and aggregation, and even nested queries. There are various approaches in the literature for interpreting user's natural language query. Rule-based systems try to identify the entities in the query, and understand the intended relationships between those entities. Recent years have seen the emergence and popularity of neural network based approaches which try to interpret the query holistically, by learning the patterns. In this tutorial, we will review these natural language interface solutions in terms of their interpretation approach, as well as the complexity of the queries they can generate. We will also discuss open research challenges.

Natural Language Interface to Database (NLIDB) eliminates the need for an end user to use complex query languages like SQL by translating the input natural language statements to SQL automatically. Although NLIDB systems have seen rapid growth of interest recently, the current state-of-the-art systems can at best handle point queries to retrieve certain column values satisfying some filters, or aggregation queries involving basic SQL aggregation functions. In this demo, we showcase our NLIDB system with extended capabilities for business applications that require complex nested SQL queries without prior training or feedback from human in-the-loop. In particular, our system uses novel algorithms that combine linguistic analysis with deep domain reasoning for solving core challenges in handling nested queries. To demonstrate the capabilities, we propose a new benchmark dataset containing realistic business intelligence queries, conforming to an ontology derived from FIBO and FRO financial ontologies. In this demo, we will showcase a wide range of complex business intelligence queries against our benchmark dataset, with increasing level of complexity. The users will be able to examine the SQL queries generated, and also will be provided with an English description of the interpretation.

Query completion systems are well studied in the context of information retrieval systems that handle keyword queries. However, Natural Language Interface to Databases (NLIDB) systems that focus on syntactically correct and semantically complete queries to obtain high precision answers require a fundamentally different approach to the query completion problem as opposed to IR systems. To the best of our knowledge, we are first to focus on the problem of query completion for NLIDB systems. In particular, we introduce a novel concept of functional partitioning of an ontology and then design algorithms to intelligently use the components obtained from functional partitioning to extend a state-of-the-art NLIDB system to produce accurate and semantically meaningful query completions in the absence of query logs. We test the proposed query completion framework on multiple benchmark datasets and demonstrate the efficacy of our technique empirically.

In this paper, we extend the state-of-the-art NLIDB system and present a dialog interface to relational databases. Dialog interface enables users to automatically exploit the semantic context of the conversation while asking natural language queries over RDBMS, thereby making it simpler to express complex questions in a natural, piece-wise manner. We propose novel ontology-driven techniques for addressing each of the dialog-specific challenges such as co-reference resolution, ellipsis resolution, and query disambiguation, and use them in determining the overall intent of the user query. We demonstrate the applicability and usefulness of dialog interface over two different domains viz. finance and healthcare.

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain.

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.