Papers by Deepak Ramachandran
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)
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Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
| Challenge: | Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks. |
| Approach: | They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue. |
| Outcome: | The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work. |
Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering (2021.acl-long)
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| Challenge: | Existing Question-Answering (QA) datasets contain unanswerable questions . however, their treatment in QA systems remains primitive . |
| Approach: | They propose a framework that provides answers based on presupposition failure over oracle behavior of existing QA systems. |
| Outcome: | The proposed system provides responses based on presupposition failure over oracle behavior of existing QA systems. |
Prompt Expansion for Adaptive Text-to-Image Generation (2024.acl-long)
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| Challenge: | Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the prompts can be repetitive. |
| Approach: | They propose a framework that takes a text query as input and outputs a set of expanded text prompts that are optimized to generate a wider variety of appealing images. |
| Outcome: | The proposed framework generates high-quality images from text prompts with less effort and is more aesthetically pleasing than baseline models. |
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2023.acl-long)
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| Challenge: | Recent advances in automated reasoning with natural text suffer from a combinatorial explosion of the search space and high failure rates for problems requiring longer chains of reasoning. |
| Approach: | They propose a Backward Chaining algorithm that decomposes reasoning into four sub-modules and implements it by few-shot prompted LLM inference. |
| Outcome: | The proposed algorithm achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets. |
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2023.acl-long)
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Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
| Challenge: | Existing DSI approaches infer latent dialog structure without access to domain knowledge. |
| Approach: | They propose a neural-symbolic approach that injects symbolic knowledge into latent space of a generative neural model. |
| Outcome: | The proposed approach boosts performance over the canonical baselines over three dialog structure induction datasets. |
Do Language Embeddings capture Scales? (2020.findings-emnlp)
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| Challenge: | Pretrained Language Models possess significant linguistic, common sense and factual knowledge, but are short of the capability required for general common-sense reasoning. |
| Approach: | They propose to train pretrained language models with a method of canonicalizing numbers . they address a task which is also pre-requisite for general common-sense reasoning . |
| Outcome: | The proposed model can answer questions about common sense and linguistics, but lacks the capability to answer questions on scalar attributes. |
How Large Are Lions? Inducing Distributions over Quantitative Attributes (P19-1)
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| Challenge: | Current NLP systems have little knowledge about quantitative attributes of objects and events. |
| Approach: | They propose to use web data to create a resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs. |
| Outcome: | The proposed method compares favorably with state-of-the-art results on existing datasets for relative comparisons of nouns and adjectives and on a new dataset. |