Papers by Pararth Shah
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
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| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning (2022.coling-1)
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| Challenge: | Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. |
| Approach: | They propose a non-autoregressive (NAR) semantic parser that introduces intent conditioning on the decoder. |
| Outcome: | The proposed model reduces inference latency while maintaining competitive parsing quality. |
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
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Rahul Goel, Waleed Ammar, Aditya Gupta, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Chuan He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, Zhou Yu
| Challenge: | PRESTO dataset contains 550K contextual multilingual conversations between humans and virtual assistants. |
| Approach: | They propose to use a dataset of 550K contextual multilingual conversations between humans and virtual assistants to study some of the more challenging aspects of parsing realistic conversations. |
| Outcome: | The dataset contains 550K contextual conversations between humans and virtual assistants. |
Memory Grounded Conversational Reasoning (D19-3)
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| Challenge: | Existing methods to retrieve and browse memories are keyword based searches or catalog based browsing systems. |
| Approach: | They propose a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. |
| Outcome: | The proposed system can perform QA over memories and make suggestions to surface related events or facts from past memories to make conversations more engaging and natural. |
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue (D19-1)
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| Challenge: | Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood. |
| Approach: | They use a goal-driven recommendation dialogue dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. |
| Outcome: | The proposed system can converse and recommend movies to humans without considering the task goal itself. |
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. |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
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| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |