Papers by Rushin Shah
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning (2022.coling-1)
Copied to clipboard
| 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. |
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog (D19-1)
Copied to clipboard
| Challenge: | Existing semantic parsers score intents and slots as labels of nesting nodes, but decode a valid tree globally. |
| Approach: | They propose a span-based semantic parser for parsing compositional utterances into Task Oriented Parse (TOP) the parsers score labels of the tree nodes covering each token span independently, but decode a valid tree globally. |
| Outcome: | The proposed parser outperforms previous methods on the TOP dataset in accuracy and training speed. |
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
Copied to clipboard
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. |
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (D18-1)
Copied to clipboard
| Challenge: | Existing work on task oriented dialog systems has limited expressive power to one intent per query and one slot label per token. |
| Approach: | They propose a hierarchical annotation scheme for semantic parsing that allows representation of compositional queries. |
| Outcome: | The proposed representation outperforms sequence-to-sequence approaches on a 44k annotated query dataset. |
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (N19-1)
Copied to clipboard
| Challenge: | a lack of multilingual training data has hindered development of conversational AI models for task-oriented tasks . a new data set of 57k annotated utterances in english, spanish, and Thai is used to evaluate cross-lingual methods . |
| Approach: | They present a data set of 57k annotated utterances in English, Spanish and Thai . they evaluate three different cross-lingual transfer methods to identify user intents and slots . |
| Outcome: | The proposed model outperforms existing methods in English, Spanish and Thai . the proposed model is based on training data from three languages . |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |