Papers by Alborz Geramifard
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations (2021.emnlp-main)
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| Challenge: | Existing task-oriented dialog datasets do not situate the dialog in the user’s multimodal context. |
| Approach: | They propose to use a dataset to study multimodal task-oriented dialogs in the shopping domain to situate them in the user’s multimodal context. |
| Outcome: | The proposed dataset includes 11K task-oriented user->assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. |
Navigating Connected Memories with a Task-oriented Dialog System (2022.emnlp-main)
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| Challenge: | Recent years have seen an increasing trend in the volume of personal media captured by users thanks to smartphones and smart glasses. |
| Approach: | They propose to use dialogs for connected memories to query media collection . they use a multimodal dialog simulator and manual paraphrasing to obtain natural language utterances. |
| Outcome: | The proposed dataset contains 11.5k userassistant dialogs grounded in simulated personal memory graphs. |
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. |
Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)
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| Challenge: | Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference. |
| Approach: | They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information. |
| Outcome: | The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference. |
Resource Constrained Dialog Policy Learning Via Differentiable Inductive Logic Programming (2020.coling-main)
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| Challenge: | Existing methods for dialog policy learning have limited data collection and data analysis. |
| Approach: | They introduce dialog policy learning via differentiable inductive logic on SimDial and MultiWoZ to address resource constrained dialog policy. |
| Outcome: | The proposed method is 100x more data efficient than state-of-the-art neural approaches on MultiWoZ while achieving similar performance metrics. |
Situated and Interactive Multimodal Conversations (2020.coling-main)
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Seungwhan Moon, Satwik Kottur, Paul Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
| Challenge: | Situated Interactive MultiModal Conversations (SIMMC) is a new direction for virtual assistants that handle multimodal inputs and perform multimodal actions. |
| Approach: | They propose to use Situated Interactive MultiModal Conversations (SIMMC) to train agents to take multimodal actions grounded in a co-evolving multimodal context. |
| Outcome: | The proposed model will be made publicly available. |
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. |