In the fact verification domain, the accuracy and efficiency of evidence retrieval are paramount. This paper presents a novel approach to enhance the fact verification process through a Multi-stage ReRanking (M-ReRank) paradigm, which addresses the inherent limitations of single-stage evidence extraction. Our methodology leverages the strengths of advanced reranking techniques, including dense retrieval models and list-aware rerankers, to optimise the retrieval and ranking of evidence of both structured and unstructured types. We demonstrate that our approach significantly outperforms previous state-of-the-art models, achieving a recall rate of 93.63% for Wikipedia pages. The proposed system not only improves the retrieval of relevant sentences and table cells but also enhances the overall verification accuracy. Through extensive experimentation on the FEVEROUS dataset, we show that our M-ReRank pipeline achieves substantial improvements in evidence extraction, particularly increasing the recall of sentences by 7.85%, tables by 8.29% and cells by 3% compared to the current state-of-the-art on the development set.

As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language models (LLMs) for optimal performance. With multi-stage processing, the pipeline demonstrates improvements over baseline models, particularly in handling complex claims that require nuanced reasoning by improved evidence extraction, question generation and veracity prediction. Through detailed experiments and ablation studies, we provide insights into the strengths and weaknesses of our approach, highlighting the critical role of evidence sufficiency and context dependency in automated fact-checking systems. Our system secured a competitive rank, 7th on the development and 12th on the test data, in the shared task, underscoring the effectiveness of our methods in addressing the challenges of real-world claim verification.

Based on the modular architecture of a task-oriented Spoken Dialogue System (SDS), the presented work focussed on constructing all the system components as statistical models with parameters learned directly from the data by resolving various language-specific and language-independent challenges. In order to understand the research questions that underlie the SLU and DST module in the perspective of Indic languages (Hindi), we collect a dialogue corpus: Hindi Dialogue Restaurant Search (HDRS) corpus and compare various state-of-the-art SLU and DST models on it. For the dialogue manager (DM), we investigate the deep-learning reinforcement learning (RL) methods, e.g. actor-critic algorithms with experience replay. Next, for the dialogue generation, we incorporated Recurrent Neural Network Language Generation (RNNLG) framework based models. For speech synthesisers as a last component in the dialogue pipeline, we not only train several TTS systems but also propose a quality assessment framework to evaluate them.