Papers by Chinnadhurai Sankar
Continual Dialogue State Tracking via Example-Guided Question Answering (2023.emnlp-main)
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Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
| Challenge: | Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services causes catastrophic forgetting. |
| Approach: | They propose to reformulate dialogue state tracking (DST) as a bundle of example-guided question answering tasks to minimize the task shift between services. |
| Outcome: | The proposed model achieves state-of-the-art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods. |
Database Search Results Disambiguation for Task-Oriented Dialog Systems (2022.naacl-main)
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Kun Qian, Satwik Kottur, Ahmad Beirami, Shahin Shayandeh, Paul Crook, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar
| Challenge: | Task-oriented dialog systems can't handle multiplesearch results when querying a database due to the lack of such scenarios in existing datasets. |
| Approach: | They propose a task that focuses on disambiguating database search results by synthetically generating turns through a pre-defined grammar and collecting human paraphrases for a subset. |
| Outcome: | The proposed task improves performance on DSR-disambiguation even in the absence of in-domain data, suggesting it can be learned as a universal dialog skill. |
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics (2022.findings-emnlp)
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Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami
| Challenge: | Recent studies have revealed the vulnerability of dialogue state tracking models to distributional shifts, resulting in poor performance. |
| Approach: | They present a toolkit for standardized and comprehensive dialogue state tracking diagnoses that provides a richer summary of strengths and weaknesses. |
| Outcome: | The proposed toolkit shows that different classes of DST models have clear strengths and weaknesses, while generation models are more promising for handling language variety and span-based classification models are robust to unseen entities. |
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset (D19-1)
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Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Ben Goodrich, Daniel Duckworth, Semih Yavuz, Amit Dubey, Kyu-Young Kim, Andy Cedilnik
| Challenge: | a lack of high quality conversational data is limiting progress in dialog systems . we present a dataset of 13,215 task-based dialogs . |
| Approach: | They propose a task-based dialog dataset which includes 13,215 task-related dialogs . they use a two-person, spoken "Wizard of Oz" approach and a "self-dialog" approach . |
| Outcome: | The taskmaster-1 dataset contains 13,215 task-based dialogs comprising six domains. |
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue (2021.acl-long)
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| Challenge: | Existing benchmarks do not have enough annotations to analyze video-grounded dialogue systems and understand their capabilities and limitations in isolation. |
| Approach: | They present a Diagnostic Dataset for Video-grounded dialogue with minimal biases and detailed annotations for the different types of reasoning over the spatio-temporal space of video. |
| Outcome: | The proposed system is based on 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video. |
Transferable Neural Projection Representations (N19-1)
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| Challenge: | Neural word embeddings require lookup and a large memory footprint making it hard to deploy on-device. |
| Approach: | They propose a skip-gram based architecture coupled with Locality-Sensitive Hashing projections to learn efficient dynamically computable representations. |
| Outcome: | The proposed model performs better than previous models on multiple NLP tasks. |
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
| Approach: | They propose to study the sensitivity of neural dialog models to unnatural perturbations . they experiment with 10 different types of perturbations on 4 multi-turn dialog datasets . |
| Outcome: | The proposed model is sensitive to unnatural changes or perturbations on 4 multi-turn dialog datasets. |
ProFormer: Towards On-Device LSH Projection Based Transformers (2021.eacl-main)
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| Challenge: | ProFormer is a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices such as mobile phones, watches and IoT. |
| Approach: | They propose a projection based transformer architecture that generates word representations on-the-fly without embedding lookup tables and a local projection attention layer that transforms the input sequence of N LSH word projections into a sequence of K representations. |
| Outcome: | The proposed architecture reduces memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead making it suitable to deploy to memory constraint devices and preserve user privacy. |
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment (2026.acl-industry)
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Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghuraman Krishnamoorthi
| Challenge: | MobileLLM-Flash is a family of foundation models for efficient on-device use with strong capabilities. |
| Approach: | They propose a method for designing on-device large language models under mobile latency constraints using hardware-in-the-loop architecture search. |
| Outcome: | The proposed model is amenable to industry-scale deployment and is compatible with mobile runtimes like Executorch. |
On-Device Text Representations Robust To Misspellings via Projections (2021.eacl-main)
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| Challenge: | Recent advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word embedding lookup tables by computing on-the fly text representations. |
| Approach: | They propose to use locality-sensitive hashing to compute on-the-fly text representations without explicit word embedding tables. |
| Outcome: | The proposed classifiers are more robust to common misspellings and perturbations of the input text compared to biLSTMs and fine-tuned BERT based methods. |
KETOD: Knowledge-Enriched Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | Existing studies treat task-oriented dialogue and chit-chat as separate domains . a new dataset is created to integrate both types of dialogue into a single system . |
| Approach: | They propose to integrate task-oriented dialogue and knowledge-grounded chit-chat into a single model by using a dataset. |
| Outcome: | The proposed models improve the performance of knowledge-enriched dialogues while maintaining a competitive task-oriented dialog performance. |