| Challenge: | a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate. |
| Approach: | They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique . |
| Outcome: | The proposed method outperforms baselines in one and two-shot settings. |
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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. |
Meta-Learning a Cross-lingual Manifold for Semantic Parsing (2023.tacl-1)
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| Challenge: | Recent work has found success with machine translation or zero-shot methods . however, these approaches can struggle to model how native speakers ask questions . |
| Approach: | They propose a meta-learning algorithm to leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. |
| Outcome: | The proposed approach trains a parser with maximum sample efficiency in six languages on ATIS. |
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Using dependency parsing for few-shot learning in distributional semantics (2022.acl-srw)
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| Challenge: | Existing methods for few-shot learning use dependency parsing information to learn meaning of rare words based on limited amount of context sentences. |
| Approach: | They propose dependency parsing for few-shot learning to learn meaning of rare words . they use word embedding models as background spaces for few shot learning . |
| Outcome: | The proposed methods enhance the additive baseline model by using dependencies. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models (2021.emnlp-main)
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| Challenge: | Existing pre-trained models need fine-tuning on tens of thousands of examples to achieve good results. |
| Approach: | They propose a framework that leverages pre-trained text-to-text models and aligns them with their pre-training framework. |
| Outcome: | The proposed framework outperforms the XLM-Roberta-large on multiple QA benchmarks and is applicable to multilingual situations. |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |
Few-Shot NLG with Pre-Trained Language Model (2020.acl-main)
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| Challenge: | Neural-based approaches to natural language generation are data-hungry and difficult to adopt in real-world applications. |
| Approach: | They propose a task of few-shot natural language generation from structured data or knowledge to generate coherent sentences from input data and language modeling to compose coherent sentences. |
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Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
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| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
| Outcome: | The proposed parser improves on one-shot parsing by 68.8% compared to baselines . it adapts the logical forms of seen utterances to fit the unseen utterant . |