Papers by Richard Shin
Language-to-Code Translation with a Single Labeled Example (2024.emnlp-main)
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Kaj Bostrom, Harsh Jhamtani, Hao Fang, Sam Thomson, Richard Shin, Patrick Xia, Benjamin Van Durme, Jason Eisner, Jacob Andreas
| Challenge: | In-Context Inverse Programming (ICIP) bootstraps a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Approach: | They propose a method for bootstrapping a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Outcome: | The proposed method outperforms baselines across nine domains and 100 examples in a “nearly unsupervised” setting. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
Learning to Retrieve Iteratively for In-Context Learning (2024.emnlp-main)
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Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme
| Challenge: | In-context learning is a powerful tool for learning large language models. |
| Approach: | They propose an iterative retrieval framework that empowers retrievers to make iterable decisions through policy optimization. |
| Outcome: | The proposed framework outperforms existing methods on semantic parsing datasets with 4M additional parameters for state encoding. |
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (2020.acl-main)
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| Challenge: | Existing semantic parsing models struggle to generalize to unseen database schemas. |
| Approach: | They propose a framework to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. |
| Outcome: | The proposed framework boosts the match accuracy to 57.2% on the spider dataset, surpassing its best counterparts by 8.7%. |
Guided K-best Selection for Semantic Parsing Annotation (2022.acl-demo)
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Anton Belyy, Chieh-yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
| Challenge: | a prototype model trained on a small amount of data is not available, leading to limited prediction performance. |
| Approach: | They propose a human-in-the-loop process that generates a set of valid candidates and allows users to quickly traverse the set and filter incorrect parses. |
| Outcome: | The proposed process can be used to efficiently traverse the candidate set and select the correct parse, with minimal modification when necessary. |
Privacy-Preserving Domain Adaptation of Semantic Parsers (2023.acl-long)
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| Challenge: | Task-oriented dialogue systems often assist users with personal or confidential matters . a lack of privacy controls prevents developers from observing actual usage . authors propose a method to generate realistic user utterances synthetically without compromising privacy . |
| Approach: | They propose a method which generates latent semantic parses and generates utterances based on the parses. |
| Outcome: | The proposed method improves MAUVE by 2.5X and parse tree function-type overlap by 1.3X . it also shows gains of 8.5% points on its accuracy with the new feature . |
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (2022.findings-acl)
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| Challenge: | a low-resource task-oriented semantic parser is limited by privacy requirements for unlabeled natural utterances. |
| Approach: | They propose a setup for low-resource task-oriented semantic parsing based on user interactions . they use structured canonical utterances, then simulating corresponding natural language to improve performance. |
| Outcome: | The proposed setup improves on a low-resource task-oriented semantic parser using utterances collected through user interactions. |
Few-Shot Semantic Parsing with Language Models Trained on Code (2022.naacl-main)
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| Challenge: | Large language models can perform semantic parsing with little training data, when prompted with in-context examples. |
| Approach: | They propose to map natural language to a controlled natural language-like representation . they find that OpenAI Codex performs better on such tasks than equivalent GPT-3 models . |
| Outcome: | The proposed model performs better on large parsing tasks than GPT-3 models on Overnight and SMCalFlow. |