Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.Comment: EMNLP 201

Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles

Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, to our knowledge, the summarization of diverse information dispersed across multiple articles about an event has not been previously investigated. The latter imposes a different set of challenges for a summarization model. In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event. To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm. The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference. Moreover, we conducted a comprehensive analysis to pinpoint the position and verbosity biases when utilizing Large Language Model (LLM)-based metrics for evaluating the coverage and faithfulness of the summaries, as well as their correlation with human assessments. We applied our findings to study how LLMs summarize multiple news articles by analyzing which type of diverse information LLMs are capable of identifying. Our analyses suggest that despite the extraordinary capabilities of LLMs in single-document summarization, the proposed task remains a complex challenge for them mainly due to their limited coverage, with GPT-4 only able to cover less than 40% of the diverse information on average

Physics Potential of the ICAL detector at the India-based Neutrino Observatory (INO)

The upcoming 50 kt magnetized iron calorimeter (ICAL) detector at the India-based Neutrino Observatory (INO) is designed to study the atmospheric neutrinos and antineutrinos separately over a wide range of energies and path lengths. The primary focus of this experiment is to explore the Earth matter effects by observing the energy and zenith angle dependence of the atmospheric neutrinos in the multi-GeV range. This study will be crucial to address some of the outstanding issues in neutrino oscillation physics, including the fundamental issue of neutrino mass hierarchy. In this document, we present the physics potential of the detector as obtained from realistic detector simulations. We describe the simulation framework, the neutrino interactions in the detector, and the expected response of the detector to particles traversing it. The ICAL detector can determine the energy and direction of the muons to a high precision, and in addition, its sensitivity to multi-GeV hadrons increases its physics reach substantially. Its charge identification capability, and hence its ability to distinguish neutrinos from antineutrinos, makes it an efficient detector for determining the neutrino mass hierarchy. In this report, we outline the analyses carried out for the determination of neutrino mass hierarchy and precision measurements of atmospheric neutrino mixing parameters at ICAL, and give the expected physics reach of the detector with 10 years of runtime. We also explore the potential of ICAL for probing new physics scenarios like CPT violation and the presence of magnetic monopoles.Comment: 139 pages, Physics White Paper of the ICAL (INO) Collaboration, Contents identical with the version published in Pramana - J. Physic

NEWS DISCOURSE STRUCTURE-GUIDED APPROACHES FOR EVENT COREFERENCE RESOLUTION

Event coreference resolution aims to determine and cluster event mentions that refer to the same real-world event. It is a relatively less studied natural language processing (NLP) task despite being crucial for various NLP applications such as topic detection and tracking, question answering, and summarization. A typical event coreference resolution system relies on scoring similarity between two event mentions in a document followed by clustering. However, event coreference chains are sparsely distributed and only certain key events that connect other peripheral events in a document are repeated to organize content and produce a coherent story. This makes manually labeling many event coreference relations very time-consuming. Furthermore, event mentions tend to appear in diverse contexts and few are accompanied by a full set of their arguments. The three challenges, the distributional sparsity of coreferential event mentions, the absence of abundant human-annotated event coreference data, and the high diversity of contexts containing coreferential event mentions, make it hard to build effective event coreference resolution systems. The primary goal of this dissertation is to develop a holistic approach that can successfully model document-level content structures to overcome the problems arising due to the sparse distribution of event coreference chains. To that end, we first study the discourse-level significance of an event that has many coreferential mentions in a document and devise a heuristics-based approach that captures several specific distributional patterns of coreferential event mentions. Inspired by the empirical improvement of the heuristics-based approach, we propose a new task of news discourse profiling, grounded in the news discourse theories, to identify document-level content structures and present a systematic method to incorporate them into an event coreference resolution system. Besides outperforming the heuristics-based model, the news discourse profiling-based system is capable of explaining the nature of correlations between coreferential event mentions and content structures. Consequently, we leverage the correlations between news discourse profiling and event coreference relations and define several rules to automatically collect event pairs from unlabeled news documents. Through both manual validation and empirical evaluations, we show that news discourse profiling additionally enables us to overcome the annotational sparsity. Overall, this dissertation contributes to the current literature on event coreference resolution by adopting news discourse structure-centric approaches that are orthogonal to supervised feature-based pairwise classifiers. News discourse structure, when incorporated through explicit constraints or used to automatically acquire data from unlabeled news documents, adds to the performance of pairwise event coreference classifiers. I hope that the work done in this dissertation potentially inspires new work on analyzing and modeling discourse structure theories to improve event coreference resolution across text genres and languages