Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (P18-1)
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| Challenge: | a recent study shows that counterfactual learning from human bandit feedback can improve neural semantic parsers . cost and difficulty of manually preparing large amounts of parses is a bottleneck for supervised learning . |
| Approach: | They propose to use human bandit feedback to apply counterfactual learning to neural parsing . they devise an easy-to-use interface to collect human feedback on semantic parses . |
| Outcome: | The proposed framework improves semantic parsers by reducing the cost of manual parsing . the proposed framework is based on human bandit feedback collected by the user . |
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| Challenge: | Existing work on interactive semantic parsing relies on human annotations to train a model . prior work relied on human-annotated feedback data, which is prohibitively expensive and not scalable . |
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Can Neural Machine Translation be Improved with User Feedback? (N18-3)
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| Challenge: | a recent study has focused on the use of explicit and implicit feedback for neural machine translation (NMT) a new study uses explicit and implied feedback to improve performance of NMT with human reinforcement. |
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| Challenge: | Existing logical forms require a user to be familiar with the underlying structure to learn a semantic parser. |
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| Challenge: | Prior studies have focused on translating utterances from high-resource languages to low-resourced languages. |
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An Imitation Game for Learning Semantic Parsers from User Interaction (2020.emnlp-main)
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| Challenge: | Existing methods for learning semantic parsers are expensive and tedious . despite the widespread applications, bootstrapping and fine-tuning is tedious a task . |
| Approach: | They propose an alternative method for learning semantic parsers directly from users . they propose an annotation-efficient imitation learning algorithm that iteratively collects new datasets . |
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| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
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Confidence Modeling for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that neural semantic parsers are difficult to interpret due to their complexity. |
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Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
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Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
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