Papers by Srikanth Madikeri
Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Dialog2Flow embeddings allow for modeling dialogs as continuous trajectories in a latent space with distinct action-related regions. |
| Approach: | They propose dialog2Flow embeddings that map dialogs to a latent space and cluster them according to their communicative and informative functions. |
| Outcome: | The proposed workflow embeddings show superior performance across domains. |
SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation (2026.eacl-demo)
Copied to clipboard
Sergio Burdisso, Séverin Baroudi, Yanis Labrak, David Grünert, Pawel Cyrta, Yiyang Chen, Srikanth Madikeri, Esaú Villatoro-tello, Ricard Marxer, Petr Motlicek
| Challenge: | SDialog is an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Approach: | They present an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Outcome: | SDialog enables more controlled, transparent, and systematic research on conversational systems. |
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR (2024.emnlp-main)
Copied to clipboard
Shashi Kumar, Srikanth Madikeri, Juan Pablo Zuluaga Gomez, Iuliia Thorbecke, Esaú Villatoro-tello, Sergio Burdisso, Petr Motlicek, Karthik S, Aravind Ganapathiraju
| Challenge: | Existing approaches to automatic speech recognition use cascaded pipelines for tasks like voice activity detection, diarization, transcription and subsequent processing. |
| Approach: | They propose a single Transducer-based model that integrates task-specific tokens into the reference text during ASR model training, streamlining inference and eliminating the need for separate NLP models. |
| Outcome: | The proposed model outperforms the existing pipeline on speaker change detection, endpointing, and NER tasks while outperforming the existing model in individual task performance. |
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
Copied to clipboard
Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |